Model Analysis
Nikhil Gupta
2019-02-13 08:44:27
sessionInfo()## R version 3.5.1 (2018-07-02)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 17134)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=English_United States.1252
## [2] LC_CTYPE=English_United States.1252
## [3] LC_MONETARY=English_United States.1252
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## loaded via a namespace (and not attached):
## [1] compiler_3.5.1 magrittr_1.5 tools_3.5.1 htmltools_0.3.6
## [5] yaml_2.2.0 Rcpp_1.0.0 stringi_1.2.4 rmarkdown_1.11
## [9] knitr_1.20 stringr_1.3.1 digest_0.6.18 evaluate_0.12User Inputs
output.var = params$output.var
transform.abs = FALSE
log.pred = FALSE
norm.pred = FALSE
if (params$trans == 1){
transform.abs == TRUE
}else if (params$trans == 2){
log.pred = TRUE
}else if (params$trans == 3){
norm.pred = TRUE
}else{
message("You have chosen no transformation")
}
eda = params$eda
algo.forward = params$algo.forward
algo.backward = params$algo.backward
algo.stepwise = params$algo.stepwise
algo.LASSO = params$algo.LASSO
algo.LARS = params$algo.LARS
algo.forward.caret = params$algo.forward.caret
algo.backward.caret = params$algo.backward.caret
algo.stepwise.caret = params$algo.stepwise.caret
algo.LASSO.caret = params$algo.LASSO.caret
algo.LARS.caret = params$algo.LARS.caret
message("Parameters used for training/prediction: ")## Parameters used for training/prediction:str(params)## List of 13
## $ output.var : chr "y3"
## $ trans : int 2
## $ eda : logi FALSE
## $ algo.forward : logi FALSE
## $ algo.backward : logi FALSE
## $ algo.stepwise : logi FALSE
## $ algo.LASSO : logi FALSE
## $ algo.LARS : logi FALSE
## $ algo.forward.caret : logi TRUE
## $ algo.backward.caret: logi TRUE
## $ algo.stepwise.caret: logi TRUE
## $ algo.LASSO.caret : logi TRUE
## $ algo.LARS.caret : logi TRUE# Setup Labels
# alt.scale.label.name = Alternate Scale variable name
# - if predicting on log, then alt.scale is normal scale
# - if predicting on normal scale, then alt.scale is log scale
if (log.pred == TRUE){
label.names = paste('log.',output.var,sep="")
alt.scale.label.name = output.var
}
if (log.pred == FALSE & norm.pred==FALSE){
label.names = output.var
alt.scale.label.name = paste('log.',output.var,sep="")
}
if (norm.pred==TRUE){
label.names = paste('norm.',output.var,sep="")
alt.scale.label.name = output.var
}Prepare Data
Read and Clean Features
features = read.csv("../../Data/features.csv")
features.highprec = read.csv("../../Data/features_highprec.csv")all.equal(features, features.highprec)## [1] "Component \"x11\": Mean relative difference: 0.001401482"
## [2] "Component \"stat9\": Mean relative difference: 0.0002946299"
## [3] "Component \"stat12\": Mean relative difference: 0.0005151515"
## [4] "Component \"stat13\": Mean relative difference: 0.001354369"
## [5] "Component \"stat18\": Mean relative difference: 0.0005141104"
## [6] "Component \"stat22\": Mean relative difference: 0.001135977"
## [7] "Component \"stat25\": Mean relative difference: 0.0001884615"
## [8] "Component \"stat29\": Mean relative difference: 0.001083691"
## [9] "Component \"stat36\": Mean relative difference: 0.00021513"
## [10] "Component \"stat37\": Mean relative difference: 0.0004578125"
## [11] "Component \"stat43\": Mean relative difference: 0.0003473684"
## [12] "Component \"stat45\": Mean relative difference: 0.0002951699"
## [13] "Component \"stat46\": Mean relative difference: 0.0009745763"
## [14] "Component \"stat47\": Mean relative difference: 8.829902e-05"
## [15] "Component \"stat55\": Mean relative difference: 0.001438066"
## [16] "Component \"stat57\": Mean relative difference: 0.0001056911"
## [17] "Component \"stat58\": Mean relative difference: 0.0004882261"
## [18] "Component \"stat60\": Mean relative difference: 0.0002408377"
## [19] "Component \"stat62\": Mean relative difference: 0.0004885106"
## [20] "Component \"stat66\": Mean relative difference: 1.73913e-06"
## [21] "Component \"stat67\": Mean relative difference: 0.0006265823"
## [22] "Component \"stat73\": Mean relative difference: 0.003846154"
## [23] "Component \"stat75\": Mean relative difference: 0.002334906"
## [24] "Component \"stat83\": Mean relative difference: 0.0005628415"
## [25] "Component \"stat86\": Mean relative difference: 0.0006104418"
## [26] "Component \"stat94\": Mean relative difference: 0.001005115"
## [27] "Component \"stat97\": Mean relative difference: 0.0003551913"
## [28] "Component \"stat98\": Mean relative difference: 0.0006157635"
## [29] "Component \"stat106\": Mean relative difference: 0.0008267717"
## [30] "Component \"stat109\": Mean relative difference: 0.0005121359"
## [31] "Component \"stat110\": Mean relative difference: 0.0007615527"
## [32] "Component \"stat111\": Mean relative difference: 0.001336134"
## [33] "Component \"stat114\": Mean relative difference: 7.680492e-05"
## [34] "Component \"stat117\": Mean relative difference: 0.0002421784"
## [35] "Component \"stat122\": Mean relative difference: 0.0006521084"
## [36] "Component \"stat123\": Mean relative difference: 8.333333e-05"
## [37] "Component \"stat125\": Mean relative difference: 0.002385135"
## [38] "Component \"stat130\": Mean relative difference: 0.001874016"
## [39] "Component \"stat132\": Mean relative difference: 0.0003193182"
## [40] "Component \"stat135\": Mean relative difference: 0.0001622517"
## [41] "Component \"stat136\": Mean relative difference: 7.722008e-05"
## [42] "Component \"stat138\": Mean relative difference: 0.0009739953"
## [43] "Component \"stat143\": Mean relative difference: 0.0004845361"
## [44] "Component \"stat146\": Mean relative difference: 0.0005821596"
## [45] "Component \"stat148\": Mean relative difference: 0.0005366922"
## [46] "Component \"stat153\": Mean relative difference: 0.0001557522"
## [47] "Component \"stat154\": Mean relative difference: 0.001351916"
## [48] "Component \"stat157\": Mean relative difference: 0.0005427928"
## [49] "Component \"stat162\": Mean relative difference: 0.002622951"
## [50] "Component \"stat167\": Mean relative difference: 0.0005905172"
## [51] "Component \"stat168\": Mean relative difference: 0.0002791096"
## [52] "Component \"stat169\": Mean relative difference: 0.0004121827"
## [53] "Component \"stat170\": Mean relative difference: 0.0004705882"
## [54] "Component \"stat174\": Mean relative difference: 0.0003822894"
## [55] "Component \"stat179\": Mean relative difference: 0.0008286604"
## [56] "Component \"stat184\": Mean relative difference: 0.0007526718"
## [57] "Component \"stat187\": Mean relative difference: 0.0005122768"
## [58] "Component \"stat193\": Mean relative difference: 4.215116e-05"
## [59] "Component \"stat199\": Mean relative difference: 0.002155844"
## [60] "Component \"stat203\": Mean relative difference: 0.0003738318"
## [61] "Component \"stat213\": Mean relative difference: 0.000667676"
## [62] "Component \"stat215\": Mean relative difference: 0.0003997955"head(features)## JobName x1 x2 x3 x4 x5 x6
## 1 Job_00001 2.0734508 4.917267 19.96188 3.520878 7.861051 1.6067589
## 2 Job_00002 2.2682543 4.955773 19.11939 19.763031 6.931355 1.3622041
## 3 Job_00003 1.7424456 2.059819 13.37912 38.829132 6.274053 2.0529845
## 4 Job_00004 0.7873555 2.613983 17.23044 64.402557 5.377652 0.9067419
## 5 Job_00005 2.3342753 4.299076 14.64883 52.537304 6.793368 2.4605792
## 6 Job_00006 1.2365089 2.795370 11.13127 96.819939 6.583971 2.3510606
## x7 x8 x9 x10 x11 x12 x13
## 1 2.979479 8.537228 1.103368 4.6089458 1.05e-07 7.995825 13.215498
## 2 2.388119 6.561461 0.588572 1.0283282 1.03e-07 7.486966 22.557224
## 3 2.043592 10.275595 4.834385 4.3872848 1.06e-07 6.350142 15.049810
## 4 2.395118 13.487331 3.340190 4.5053501 9.47e-08 9.548698 17.170635
## 5 2.891535 9.362389 1.246039 1.7333300 1.01e-07 9.596095 5.794567
## 6 1.247838 7.033354 1.852231 0.4839371 1.07e-07 3.810983 23.863169
## x14 x15 x16 x17 x18 x19 x20
## 1 4.377983 0.2370623 6.075459 3.988347 4.767475 2.698775 1.035893
## 2 2.059315 0.5638121 6.903891 4.152054 6.849232 9.620731 1.915288
## 3 3.260057 2.0603445 8.424065 4.489893 3.493591 4.715386 1.558103
## 4 3.093478 1.8806034 11.189792 2.134271 5.588357 5.107871 1.489588
## 5 3.943076 1.5820830 7.096742 3.563378 7.765610 1.360272 1.240283
## 6 1.280562 1.1733382 7.062051 1.341864 7.748325 5.009365 1.725179
## x21 x22 x23 stat1 stat2 stat3 stat4
## 1 42.36548 1.356213 2.699796 2.3801832 0.1883335 -1.2284011 -0.5999233
## 2 26.63295 4.053961 2.375127 -1.4069480 1.8140973 1.6204884 2.6422672
## 3 20.09693 3.079888 4.488420 -0.7672566 -0.1230289 1.1415752 2.9805934
## 4 32.60415 1.355396 3.402398 0.4371202 -1.9355906 0.9028624 -1.6025400
## 5 44.58361 1.940301 2.249011 2.4492466 -0.6172000 -2.5520642 -2.1485929
## 6 28.75102 2.500499 5.563972 -1.7899084 1.8853619 2.4154840 -2.6022179
## stat5 stat6 stat7 stat8 stat9 stat10
## 1 0.148893163 -0.6622978 -2.4851868 0.3647782 2.5364335 2.92067981
## 2 1.920768980 1.7411555 -1.9599979 -2.0190558 -1.3732762 -0.31642506
## 3 2.422584300 -0.4166040 2.2205689 -2.6741531 0.4844292 2.73379230
## 4 -0.001795933 -0.6946563 -0.3693534 -0.9709467 1.7960306 0.74771154
## 5 -2.311132430 -1.0166832 2.7269876 1.5424492 -1.3156369 -0.09767897
## 6 -1.785491470 -1.8599915 1.4875095 2.0188572 -1.4892503 -1.41103566
## stat11 stat12 stat13 stat14 stat15 stat16
## 1 -2.3228905 -2.480567 -0.6335157 -0.3650149 -0.5322812 0.6029300
## 2 -0.8547903 1.119316 0.7227427 0.2121097 -0.1452281 -2.0361528
## 3 -2.1821580 2.865401 -2.9756081 2.9871745 1.9539525 -1.8857163
## 4 1.3982378 1.856765 -1.0379983 2.3341896 2.3057184 -2.8947697
## 5 0.9567220 2.567549 0.3184886 1.0307668 0.1644241 -0.6613821
## 6 0.5341771 -1.461822 0.4402476 -1.9282095 -0.3680157 1.8188807
## stat17 stat18 stat19 stat20 stat21 stat22
## 1 -1.04516208 2.3544915 2.4049001 0.2633883 -0.9788178 1.7868229
## 2 0.09513074 0.4727738 1.8899702 2.7892542 -1.3919091 -1.7198164
## 3 0.40285346 1.4655282 -1.4952788 2.9162340 -2.3893208 2.8161423
## 4 2.97446084 2.3896182 2.3083484 -1.1894441 -2.1982553 1.3666242
## 5 -0.98465055 0.6900643 1.5894209 -2.1204538 1.7961155 -0.9362189
## 6 -1.45726359 -2.1139548 -0.3964904 1.1764175 -2.9100556 -2.1359294
## stat23 stat24 stat25 stat26 stat27 stat28
## 1 -2.3718851 2.8580718 -0.4719713 -2.817086 -0.9518474 2.88892484
## 2 -2.3293245 1.5577759 -1.9569720 1.554194 -0.5081459 -1.58715141
## 3 -2.5402296 0.1422861 0.3572798 -1.051886 -2.1541717 0.03074004
## 4 -1.9679050 -1.4077642 2.5097435 1.683121 -0.2549745 -2.90384054
## 5 2.0523429 -2.2084844 -1.9280857 -2.116736 1.8180779 -1.42167580
## 6 0.2184991 -0.7599817 2.6880329 -2.903350 -1.0733233 -2.92416644
## stat29 stat30 stat31 stat32 stat33 stat34
## 1 0.7991088 -2.0059092 -0.2461502 0.6482101 -2.87462163 -0.3601543
## 2 1.9758110 -0.3874187 1.3566630 2.6493473 2.28463054 1.8591728
## 3 -0.4460218 1.0279679 1.3998452 -1.0183365 1.41109037 -2.4183984
## 4 1.0571996 2.5588036 -2.9830337 -1.1299983 0.05470414 -1.5566561
## 5 0.8854889 2.2774174 2.6499031 2.3053405 -2.39148426 -1.8272992
## 6 -0.8405267 0.1311945 0.4321289 -2.9622040 -2.55387473 2.6396458
## stat35 stat36 stat37 stat38 stat39 stat40
## 1 2.4286051 -0.5420244 -2.6782637 -2.8874269 -0.8945006 1.1749642
## 2 1.3709245 -1.3714181 1.3901204 1.2273489 -0.8934880 1.0540369
## 3 -0.9805572 2.0571353 0.8845031 2.0574493 1.1222047 1.8528618
## 4 1.0969149 -2.2820673 1.8852408 0.5391517 2.7334342 -0.4372566
## 5 -1.0971669 1.4867796 -2.3738465 -0.3743561 1.4266498 1.2551680
## 6 0.4584349 -2.2696617 -0.9935142 -0.5350499 -0.7874799 2.0009417
## stat41 stat42 stat43 stat44 stat45 stat46
## 1 -1.0474428 -1.3909023 2.54110503 -1.4320793 0.6298335 -2.09296608
## 2 2.5380247 1.6476108 0.44128850 -2.5049477 1.2726039 1.72492969
## 3 1.1477574 0.2288794 0.08891252 2.3044751 -0.7735722 -0.07302936
## 4 -1.3808300 -2.7900956 2.38297582 0.1686397 -2.1591296 1.60828602
## 5 0.2257536 1.9542116 2.66429019 0.8026123 -1.5521187 1.61751962
## 6 -1.3364114 -2.2898803 2.80735397 -0.8413086 1.0057797 -1.50653386
## stat47 stat48 stat49 stat50 stat51 stat52
## 1 -2.8318939 2.1445766 0.5668035 0.1544579 0.6291955 2.2197027
## 2 -0.5804687 -1.3689737 1.4908396 1.2465997 0.8896304 -2.6024318
## 3 0.7918019 1.5712964 1.1038082 -0.2545658 -2.1662638 0.2660159
## 4 -1.8894132 0.5680230 -0.7023218 -0.3972188 0.1578027 2.1770194
## 5 2.1088455 -2.7195437 2.1961412 -0.2615084 1.2109556 0.8260623
## 6 -1.4400891 -0.9421459 -1.7324599 -2.1720727 -2.8129435 0.6958785
## stat53 stat54 stat55 stat56 stat57 stat58
## 1 2.176805 0.5546907 -2.19704103 -0.2884173 1.3232913 -1.32824039
## 2 -2.107441 1.3864788 0.08781975 1.9998228 0.8014438 -0.26979154
## 3 1.234197 2.1337581 1.65231645 -0.4388691 -0.1811156 2.11277962
## 4 2.535406 -2.1387620 0.12856023 -1.9906180 0.9626449 1.65232646
## 5 -2.457080 2.1633499 0.60441124 2.5449364 -1.4978440 2.60542655
## 6 2.003033 -0.5379940 -2.19647264 -1.1954677 -0.5974466 -0.04703835
## stat59 stat60 stat61 stat62 stat63 stat64
## 1 1.24239659 -2.5798278 1.327928 1.68560362 0.6284891 -1.6798652
## 2 0.06379301 0.9465770 1.116928 0.03128772 -2.1944375 0.3382609
## 3 0.93223447 2.4597080 0.465251 -1.71033382 -0.5156728 1.8276784
## 4 -0.29840910 0.7273473 -2.313066 -1.47696018 2.5910559 -1.5127999
## 5 -1.17610002 -1.7948418 -2.669305 0.17813617 2.8956099 2.9411416
## 6 -1.01793981 0.2817057 2.228023 -0.86494124 -0.9747949 -0.1569053
## stat65 stat66 stat67 stat68 stat69 stat70
## 1 -2.9490898 -0.3325469 1.5745990 -2.2978280 1.5451891 -1.345990
## 2 -1.1174885 -1.5728682 -2.9229002 0.2658547 -1.9616533 2.506130
## 3 -0.2231264 -0.4503301 0.7932286 -1.2453773 -2.2309763 2.309761
## 4 -0.3522418 -2.0720532 0.9442933 2.9212906 0.5100371 -2.441108
## 5 -2.1648991 1.2002029 2.8266985 0.7461294 1.6772674 -1.280000
## 6 -2.2295458 1.1446493 0.2024925 -0.2983998 -2.8203752 1.224030
## stat71 stat72 stat73 stat74 stat75 stat76
## 1 1.0260956 2.1071210 2.6625669 -2.8924677 -0.02132523 -2.5058765
## 2 0.3525076 1.6922342 -1.2167022 -1.7271879 2.21176434 1.9329683
## 3 -2.1799035 -2.2645276 0.1415582 0.9887453 1.95592320 0.2951785
## 4 -2.4051409 2.0876484 -0.8632146 0.4011389 -1.16986716 -1.2391174
## 5 1.3538754 -0.8089395 -0.5122626 -2.1696892 1.07344925 2.6696169
## 6 -2.8073371 -1.4450488 0.5481212 -1.4381690 0.80917043 -0.1365944
## stat77 stat78 stat79 stat80 stat81 stat82
## 1 -2.5631845 -2.40331340 0.38416120 -1.2564875 -0.1550840 -1.1762617
## 2 -0.4462085 0.38400793 1.80483031 -0.8387642 0.7624431 0.9936900
## 3 1.6757870 -1.81900752 2.70904708 -0.3201959 2.5754235 1.6346260
## 4 -2.1012006 -2.24691081 1.78056848 1.0323739 1.0762523 2.1343851
## 5 -2.5736733 -1.99958372 -0.05388495 -2.5630073 -2.8783002 -0.5752426
## 6 1.6143972 0.03233746 2.90835762 1.4000487 2.9275615 -2.8503830
## stat83 stat84 stat85 stat86 stat87 stat88
## 1 1.2840565 -2.6794965 1.3956039 -1.5290235 2.221152 2.3794982
## 2 -0.2380048 1.9314318 -1.6747955 -0.3663656 1.582659 -0.5222489
## 3 -0.9150769 -1.5520337 2.4186287 2.7273662 1.306642 0.1320062
## 4 -2.5824408 -2.7775943 0.5085060 0.4689015 2.053348 0.7957955
## 5 -1.0017741 -0.2009138 0.3770109 2.4335201 -1.118058 1.3953410
## 6 2.4891765 2.9931953 -1.4171852 0.3905659 -1.856119 -2.1690490
## stat89 stat90 stat91 stat92 stat93 stat94
## 1 -0.9885110 -0.8873261 -2.7810929 -1.53325891 2.6002395 1.8890998
## 2 0.9982028 -1.2382015 -0.1574496 0.41086048 -0.5412626 -0.2421387
## 3 0.5956759 1.6871066 2.2452753 2.74279594 -1.5860478 2.9393122
## 4 2.0902634 2.1752586 -2.0677712 -2.37861037 1.1653302 0.1500632
## 5 2.9820614 0.8111660 -0.7842287 0.03766387 -1.1681970 2.1217251
## 6 -1.7428021 0.1579032 1.7456742 -0.36858466 -0.1304616 -1.4555819
## stat95 stat96 stat97 stat98 stat99 stat100
## 1 -2.6056035 -0.5814857 2.57652426 -2.3297751 2.6324007 1.445827
## 2 -2.0271583 -0.9126074 2.49582648 0.9745382 1.1339203 -2.549544
## 3 0.3823181 -0.6324139 2.46221566 1.1151560 0.4624891 0.107072
## 4 2.6414623 -0.6630505 2.10394859 1.2627635 0.4861740 1.697012
## 5 1.4642254 2.6485956 -0.07699547 0.6219473 -1.8815142 -2.685463
## 6 1.8937331 -0.4690555 1.04671776 -0.5879866 -0.9766789 2.405940
## stat101 stat102 stat103 stat104 stat105 stat106
## 1 -2.1158021 2.603936 1.7745128 -1.8903574 -1.8558655 1.0122044
## 2 -2.7998588 -2.267895 0.5336456 -0.2859477 -0.5196246 -0.9417582
## 3 0.7969509 -1.744906 -0.7960327 1.9767258 -0.2007264 -0.7872376
## 4 1.7071959 -1.540221 1.6770362 1.5395796 -0.4855365 -1.2894115
## 5 -1.4627420 -1.700983 2.4376490 0.2731541 1.5275587 1.3256483
## 6 2.6888530 1.090155 2.0769854 1.9615480 1.8689761 2.8975825
## stat107 stat108 stat109 stat110 stat111 stat112
## 1 1.954508 -0.3376471 2.503084 0.3099165 2.7209847 -2.3911204
## 2 -2.515160 0.3998704 -1.077093 2.4228268 -0.7759693 0.2513882
## 3 1.888827 1.5819857 -2.066659 -2.0008364 0.6997684 2.6157095
## 4 1.076395 -1.8524148 -2.689204 1.0985872 1.2389493 2.1018629
## 5 2.828866 -1.8590252 -2.424163 1.4391942 -0.6173239 -1.5218846
## 6 -1.419639 0.7888914 1.996463 0.9813507 0.9034198 1.3810679
## stat113 stat114 stat115 stat116 stat117 stat118
## 1 -1.616161 1.0878664 0.9860094 -0.06288462 -1.013501 -1.2212842
## 2 -1.554771 1.8683100 0.4880588 -0.63865489 -1.610217 -1.7713343
## 3 -2.679801 -2.9486952 1.7753417 0.90311784 -1.318836 -0.1429040
## 4 2.459229 -0.5584171 0.4419581 -0.09586351 0.595442 0.2883342
## 5 -2.102200 1.6300170 -2.3498287 1.36771894 -1.912202 -0.2563821
## 6 -1.835037 0.6577786 -2.9928374 2.13540316 -1.437299 -0.9570006
## stat119 stat120 stat121 stat122 stat123 stat124
## 1 2.9222729 1.9151262 1.6686068 2.0061224 1.5723072 0.78819227
## 2 2.1828208 0.8283178 -2.4458632 1.7133740 1.1393738 -0.07182054
## 3 0.9721319 1.2723130 2.8002086 2.7670381 -2.2252586 2.17499113
## 4 -1.9327896 -2.5369370 1.7835028 1.0262097 -1.8790983 -0.43639564
## 5 1.3230809 -2.8145256 -0.9547533 -2.0435417 -0.2758764 -1.85668027
## 6 0.1720700 -1.4568460 1.4115051 -0.9878145 2.3895061 -2.33730745
## stat125 stat126 stat127 stat128 stat129 stat130
## 1 1.588372 1.1620011 -0.2474264 1.650328 2.5147598 0.37283245
## 2 -1.173771 0.8162020 0.3510315 -1.263667 1.7245284 -0.72852904
## 3 -1.503497 -0.5656394 2.8040256 -2.139287 -1.7221642 2.17899609
## 4 1.040967 -2.9039600 0.3103742 1.462339 -1.2940350 -2.95015502
## 5 -2.866184 1.6885070 -2.2525666 -2.628631 1.8581577 2.80127025
## 6 -1.355111 1.5017927 0.4295921 -0.580415 0.9851009 -0.03773117
## stat131 stat132 stat133 stat134 stat135 stat136
## 1 -0.09028241 0.5194538 2.8478346 2.6664724 -2.0206311 1.398415090
## 2 -0.53045595 1.4134049 2.9180586 0.3299096 1.4784122 -1.278896090
## 3 1.35843194 0.2279946 0.3532595 0.6138676 -0.3443284 0.057763811
## 4 -1.92450273 1.2698178 -1.5299660 -2.6083462 1.1665530 -0.187791914
## 5 1.49036849 2.6337729 -2.3206244 0.4978287 -1.7397571 0.001200184
## 6 -0.64642709 -1.9256228 1.7032650 -0.9152725 -0.3188055 2.155395980
## stat137 stat138 stat139 stat140 stat141 stat142
## 1 -1.2794871 0.4064890 -0.4539998 2.6660173 -1.8375313 0.4711883
## 2 -2.7709017 -1.6303773 -1.9025910 0.2572918 0.6612002 1.4764348
## 3 -1.1930757 -0.1051243 -0.5108380 -1.0879666 2.4969513 -0.9477230
## 4 -1.2318919 2.2348571 0.1788580 -1.5851788 -1.2384283 -2.1859181
## 5 1.8685058 2.7229517 -2.9077182 2.6606939 -1.5963592 -2.2213492
## 6 -0.4807318 -1.2117369 -0.9358531 -2.5100758 -2.3803916 -0.7096854
## stat143 stat144 stat145 stat146 stat147 stat148
## 1 1.9466263 2.2689433 -0.3597288 -0.6551386 1.65438592 0.6404466
## 2 1.3156421 2.4459090 -0.3790028 1.4858465 -0.07784461 1.0096149
## 3 0.1959563 2.3062942 1.8459278 2.6848175 -2.70935774 -1.2093409
## 4 1.7633296 -2.8171508 2.0902622 -2.6625464 -1.12600601 -2.1926479
## 5 0.3885758 1.8160636 2.8257299 -1.4526173 1.60679603 2.3807991
## 6 0.7623450 0.2692145 -2.4307463 -2.1244523 -2.67803812 -1.5273387
## stat149 stat150 stat151 stat152 stat153 stat154
## 1 0.1583575 0.4755351 0.3213410 2.0241520 1.5720103 -0.1825875
## 2 -0.4311406 2.9577663 0.6937252 0.1397280 0.3775735 -1.1012636
## 3 -0.8352824 2.5716205 1.7528236 0.4326277 -2.2334397 -2.6265771
## 4 -2.8069143 1.8813509 2.3358023 0.1015632 1.2117474 -1.3714278
## 5 -1.6166265 1.1112266 -1.1998471 2.9316769 -2.1676455 -0.3411089
## 6 -0.2265472 2.7264354 -1.6746094 -2.3376281 -1.7022788 -1.2352397
## stat155 stat156 stat157 stat158 stat159 stat160
## 1 -1.139657 0.07061254 0.5893906 -1.9920996 -2.83714366 2.249398
## 2 -2.041093 0.74047768 2.5415072 -1.2697256 -1.64364433 -2.448922
## 3 -1.219507 -0.55198693 0.4046920 1.2098547 -0.90412390 -1.934093
## 4 2.992191 2.33222485 2.0622969 -0.6714653 2.76836085 -1.431120
## 5 -2.362356 -1.23906672 0.4746319 -0.7849202 0.69399995 2.052411
## 6 -1.604499 1.31051409 -0.5164744 0.6288667 0.07899523 -2.287402
## stat161 stat162 stat163 stat164 stat165 stat166
## 1 1.7182635 -1.2323593 2.7350423 1.0707235 1.1621544 0.9493989
## 2 -0.6247674 2.6740098 2.8211024 1.5561292 -1.1027147 1.0519739
## 3 -0.6230453 -0.7993517 -2.8318374 -1.1148673 1.4261659 0.5294309
## 4 1.7644744 0.1696584 1.2653207 0.6621516 0.9470508 0.1985014
## 5 -1.2070210 0.7243784 0.9736322 2.7426259 -2.6862383 1.6840212
## 6 2.3705316 -2.1667893 -0.2516685 -0.8425958 -1.9099342 -2.8607297
## stat167 stat168 stat169 stat170 stat171 stat172
## 1 0.1146510 2.3872008 1.1180918 -0.95370555 -2.25076509 0.2348182
## 2 1.0760417 -2.0449336 0.9715676 -0.40173489 -0.11953555 -2.3107369
## 3 1.1735898 1.3860190 -2.2894719 0.06350347 0.29191551 -1.6079744
## 4 2.5511832 0.5446648 1.2694012 -0.84571201 0.79789722 0.2623538
## 5 2.2900002 2.6289782 -0.2783571 1.39032829 -0.55532032 1.0499046
## 6 -0.7513983 2.9617066 -2.2119520 -1.71958113 -0.01452018 -0.2751517
## stat173 stat174 stat175 stat176 stat177 stat178
## 1 1.79366076 -1.920206 -0.38841942 0.8530301 1.64532077 -1.1354179
## 2 -0.07484659 1.337846 2.20911694 0.9616837 -2.80810070 -2.1136749
## 3 -1.05521810 -1.483741 0.06148359 2.3066039 -0.34688616 1.1840581
## 4 0.31460321 1.195741 2.97633862 1.1685091 -0.06346265 1.4205489
## 5 -1.39428365 2.458523 0.64836472 -1.0396386 -0.57828104 -0.5006818
## 6 2.31844401 1.239864 -2.06490874 0.7696204 -1.77586019 2.0855925
## stat179 stat180 stat181 stat182 stat183 stat184
## 1 2.0018647 0.1476815 -1.27279520 1.9181504 -0.5297624 -2.9718938
## 2 -2.1351449 2.9012582 -1.09914911 -2.5488517 -2.8377736 1.4073374
## 3 -1.7819908 2.9902627 0.81908613 0.2503852 0.3712984 -2.1714024
## 4 -0.1026974 -2.4763253 -2.52645421 1.3096315 2.1458161 -1.5228094
## 5 -2.2298794 2.4465680 -0.70346898 -1.6997617 2.9178164 -0.3615532
## 6 -1.1168108 1.5552123 -0.01361342 1.7338791 -1.1104763 0.1882416
## stat185 stat186 stat187 stat188 stat189 stat190
## 1 -0.1043832 -1.5047463 2.700351 -2.4780862 -1.9078265 0.9978108
## 2 -2.0310574 -0.5380074 -1.963275 -1.2221278 -2.4290681 -1.9515115
## 3 2.6727278 1.2688179 -1.399018 -2.9612138 2.6456394 2.0073323
## 4 -2.7796295 2.0682354 2.243727 0.4296881 0.1931333 2.2710960
## 5 -0.6231265 2.5833981 2.229041 0.8139584 1.4544131 1.8886451
## 6 2.7204690 -2.4469144 -1.421998 1.7477882 -0.1481806 0.6011560
## stat191 stat192 stat193 stat194 stat195 stat196
## 1 -0.6644351 2.6270833 -1.1094601 -2.4200392 2.870713 -0.6590932
## 2 -0.6483142 1.4519118 -0.1963493 -2.3025322 1.255608 2.1617947
## 3 -1.5457382 -0.2977442 -1.7045015 0.7962404 -1.696063 -1.4771117
## 4 -1.1780495 -2.9747574 -1.1471518 -1.2377013 -1.010672 -2.6055975
## 5 2.8813178 -1.8964081 -1.2653487 -1.7839754 -2.872581 2.3033464
## 6 0.4437973 0.6599325 -1.4029555 -2.3118258 -1.792232 1.3934380
## stat197 stat198 stat199 stat200 stat201 stat202
## 1 -0.83056986 0.9550526 -1.7025776 -2.8263099 -0.7023998 0.2272806
## 2 -1.42178249 -1.2471864 2.5723093 -0.0233496 -1.8975239 1.9472262
## 3 -0.19233958 -0.5161456 0.0279946 -1.2333704 -2.9672263 -2.8666208
## 4 -1.23145902 1.4728470 -0.4562025 -2.2983441 -1.5101184 0.2530525
## 5 1.85018563 -1.8269292 -0.6337969 -2.1473246 0.9909850 1.0950903
## 6 -0.09311061 0.5144456 -2.8178268 -2.7555969 -2.3546004 -1.0558939
## stat203 stat204 stat205 stat206 stat207 stat208
## 1 1.166631220 0.007453276 2.9961641 1.5327307 -2.2293356 -0.9946009
## 2 -0.235396504 2.132749800 0.3707606 1.5604026 -1.0089217 2.1474257
## 3 0.003180946 2.229793310 2.7354040 0.8992231 2.9694967 2.3081024
## 4 -0.474482715 -1.584772230 -2.3224132 -0.9409741 -2.3179255 0.8032548
## 5 2.349412920 -1.276320220 -2.0203719 -1.1733509 1.0371852 -2.5086207
## 6 0.727436960 -0.960191786 -0.8964998 -1.6406623 -0.2330488 1.7993879
## stat209 stat210 stat211 stat212 stat213 stat214
## 1 -2.2182105 -1.4099774 -1.656754 2.6602585 -2.9270992 1.1240714
## 2 -2.8932488 -1.1641679 -2.605423 -1.5650513 2.9523673 2.0266318
## 3 -1.8279589 0.0472350 -2.026734 2.5054367 0.9903042 0.3274105
## 4 -1.0878067 0.1171303 2.645891 -1.6775225 1.3452160 1.4694063
## 5 -0.8158175 0.4060950 0.912256 0.2925677 2.1610141 0.5679936
## 6 -2.2664354 -0.2061083 -1.435174 2.6645632 0.4216259 -0.6419122
## stat215 stat216 stat217
## 1 -2.7510750 -0.5501796 1.2638469
## 2 2.8934650 -2.4099574 -1.2411407
## 3 -1.0947676 1.2852937 1.5411530
## 4 0.6343777 0.1345372 2.9102673
## 5 0.9908702 1.7909757 -2.0902610
## 6 -2.8113887 -1.0624912 0.2765074head(features.highprec)## JobName x1 x2 x3 x4 x5 x6
## 1 Job_00001 2.0734508 4.917267 19.96188 3.520878 7.861051 1.6067589
## 2 Job_00002 2.2682543 4.955773 19.11939 19.763031 6.931355 1.3622041
## 3 Job_00003 1.7424456 2.059819 13.37912 38.829132 6.274053 2.0529845
## 4 Job_00004 0.7873555 2.613983 17.23044 64.402557 5.377652 0.9067419
## 5 Job_00005 2.3342753 4.299076 14.64883 52.537304 6.793368 2.4605792
## 6 Job_00006 1.2365089 2.795370 11.13127 96.819939 6.583971 2.3510606
## x7 x8 x9 x10 x11 x12 x13
## 1 2.979479 8.537228 1.103368 4.6089458 1.050025e-07 7.995825 13.215498
## 2 2.388119 6.561461 0.588572 1.0283282 1.034518e-07 7.486966 22.557224
## 3 2.043592 10.275595 4.834385 4.3872848 1.062312e-07 6.350142 15.049810
## 4 2.395118 13.487331 3.340190 4.5053501 9.471887e-08 9.548698 17.170635
## 5 2.891535 9.362389 1.246039 1.7333300 1.010552e-07 9.596095 5.794567
## 6 1.247838 7.033354 1.852231 0.4839371 1.071662e-07 3.810983 23.863169
## x14 x15 x16 x17 x18 x19 x20
## 1 4.377983 0.2370623 6.075459 3.988347 4.767475 2.698775 1.035893
## 2 2.059315 0.5638121 6.903891 4.152054 6.849232 9.620731 1.915288
## 3 3.260057 2.0603445 8.424065 4.489893 3.493591 4.715386 1.558103
## 4 3.093478 1.8806034 11.189792 2.134271 5.588357 5.107871 1.489588
## 5 3.943076 1.5820830 7.096742 3.563378 7.765610 1.360272 1.240283
## 6 1.280562 1.1733382 7.062051 1.341864 7.748325 5.009365 1.725179
## x21 x22 x23 stat1 stat2 stat3 stat4
## 1 42.36548 1.356213 2.699796 2.3801832 0.1883335 -1.2284011 -0.5999233
## 2 26.63295 4.053961 2.375127 -1.4069480 1.8140973 1.6204884 2.6422672
## 3 20.09693 3.079888 4.488420 -0.7672566 -0.1230289 1.1415752 2.9805934
## 4 32.60415 1.355396 3.402398 0.4371202 -1.9355906 0.9028624 -1.6025400
## 5 44.58361 1.940301 2.249011 2.4492466 -0.6172000 -2.5520642 -2.1485929
## 6 28.75102 2.500499 5.563972 -1.7899084 1.8853619 2.4154840 -2.6022179
## stat5 stat6 stat7 stat8 stat9 stat10
## 1 0.148893163 -0.6622978 -2.4851868 0.3647782 2.5364335 2.92067981
## 2 1.920768980 1.7411555 -1.9599979 -2.0190558 -1.3732762 -0.31642506
## 3 2.422584300 -0.4166040 2.2205689 -2.6741531 0.4844292 2.73379230
## 4 -0.001795933 -0.6946563 -0.3693534 -0.9709467 1.7960306 0.74771154
## 5 -2.311132430 -1.0166832 2.7269876 1.5424492 -1.3156369 -0.09767897
## 6 -1.785491470 -1.8599915 1.4875095 2.0188572 -1.4892503 -1.41103566
## stat11 stat12 stat13 stat14 stat15 stat16
## 1 -2.3228905 -2.480567 -0.6335157 -0.3650149 -0.5322812 0.6029300
## 2 -0.8547903 1.119316 0.7227427 0.2121097 -0.1452281 -2.0361528
## 3 -2.1821580 2.865401 -2.9756081 2.9871745 1.9539525 -1.8857163
## 4 1.3982378 1.856765 -1.0379983 2.3341896 2.3057184 -2.8947697
## 5 0.9567220 2.567549 0.3184886 1.0307668 0.1644241 -0.6613821
## 6 0.5341771 -1.461822 0.4402476 -1.9282095 -0.3680157 1.8188807
## stat17 stat18 stat19 stat20 stat21 stat22
## 1 -1.04516208 2.3544915 2.4049001 0.2633883 -0.9788178 1.7868229
## 2 0.09513074 0.4727738 1.8899702 2.7892542 -1.3919091 -1.7198164
## 3 0.40285346 1.4655282 -1.4952788 2.9162340 -2.3893208 2.8161423
## 4 2.97446084 2.3896182 2.3083484 -1.1894441 -2.1982553 1.3666242
## 5 -0.98465055 0.6900643 1.5894209 -2.1204538 1.7961155 -0.9362189
## 6 -1.45726359 -2.1139548 -0.3964904 1.1764175 -2.9100556 -2.1359294
## stat23 stat24 stat25 stat26 stat27 stat28
## 1 -2.3718851 2.8580718 -0.4719713 -2.817086 -0.9518474 2.88892484
## 2 -2.3293245 1.5577759 -1.9569720 1.554194 -0.5081459 -1.58715141
## 3 -2.5402296 0.1422861 0.3572798 -1.051886 -2.1541717 0.03074004
## 4 -1.9679050 -1.4077642 2.5097435 1.683121 -0.2549745 -2.90384054
## 5 2.0523429 -2.2084844 -1.9280857 -2.116736 1.8180779 -1.42167580
## 6 0.2184991 -0.7599817 2.6880329 -2.903350 -1.0733233 -2.92416644
## stat29 stat30 stat31 stat32 stat33 stat34
## 1 0.7991088 -2.0059092 -0.2461502 0.6482101 -2.87462163 -0.3601543
## 2 1.9758110 -0.3874187 1.3566630 2.6493473 2.28463054 1.8591728
## 3 -0.4460218 1.0279679 1.3998452 -1.0183365 1.41109037 -2.4183984
## 4 1.0571996 2.5588036 -2.9830337 -1.1299983 0.05470414 -1.5566561
## 5 0.8854889 2.2774174 2.6499031 2.3053405 -2.39148426 -1.8272992
## 6 -0.8405267 0.1311945 0.4321289 -2.9622040 -2.55387473 2.6396458
## stat35 stat36 stat37 stat38 stat39 stat40
## 1 2.4286051 -0.5420244 -2.6782637 -2.8874269 -0.8945006 1.1749642
## 2 1.3709245 -1.3714181 1.3901204 1.2273489 -0.8934880 1.0540369
## 3 -0.9805572 2.0571353 0.8845031 2.0574493 1.1222047 1.8528618
## 4 1.0969149 -2.2820673 1.8852408 0.5391517 2.7334342 -0.4372566
## 5 -1.0971669 1.4867796 -2.3738465 -0.3743561 1.4266498 1.2551680
## 6 0.4584349 -2.2696617 -0.9935142 -0.5350499 -0.7874799 2.0009417
## stat41 stat42 stat43 stat44 stat45 stat46
## 1 -1.0474428 -1.3909023 2.54110503 -1.4320793 0.6298335 -2.09296608
## 2 2.5380247 1.6476108 0.44128850 -2.5049477 1.2726039 1.72492969
## 3 1.1477574 0.2288794 0.08891252 2.3044751 -0.7735722 -0.07302936
## 4 -1.3808300 -2.7900956 2.38297582 0.1686397 -2.1591296 1.60828602
## 5 0.2257536 1.9542116 2.66429019 0.8026123 -1.5521187 1.61751962
## 6 -1.3364114 -2.2898803 2.80735397 -0.8413086 1.0057797 -1.50653386
## stat47 stat48 stat49 stat50 stat51 stat52
## 1 -2.8318939 2.1445766 0.5668035 0.1544579 0.6291955 2.2197027
## 2 -0.5804687 -1.3689737 1.4908396 1.2465997 0.8896304 -2.6024318
## 3 0.7918019 1.5712964 1.1038082 -0.2545658 -2.1662638 0.2660159
## 4 -1.8894132 0.5680230 -0.7023218 -0.3972188 0.1578027 2.1770194
## 5 2.1088455 -2.7195437 2.1961412 -0.2615084 1.2109556 0.8260623
## 6 -1.4400891 -0.9421459 -1.7324599 -2.1720727 -2.8129435 0.6958785
## stat53 stat54 stat55 stat56 stat57 stat58
## 1 2.176805 0.5546907 -2.19704103 -0.2884173 1.3232913 -1.32824039
## 2 -2.107441 1.3864788 0.08781975 1.9998228 0.8014438 -0.26979154
## 3 1.234197 2.1337581 1.65231645 -0.4388691 -0.1811156 2.11277962
## 4 2.535406 -2.1387620 0.12856023 -1.9906180 0.9626449 1.65232646
## 5 -2.457080 2.1633499 0.60441124 2.5449364 -1.4978440 2.60542655
## 6 2.003033 -0.5379940 -2.19647264 -1.1954677 -0.5974466 -0.04703835
## stat59 stat60 stat61 stat62 stat63 stat64
## 1 1.24239659 -2.5798278 1.327928 1.68560362 0.6284891 -1.6798652
## 2 0.06379301 0.9465770 1.116928 0.03128772 -2.1944375 0.3382609
## 3 0.93223447 2.4597080 0.465251 -1.71033382 -0.5156728 1.8276784
## 4 -0.29840910 0.7273473 -2.313066 -1.47696018 2.5910559 -1.5127999
## 5 -1.17610002 -1.7948418 -2.669305 0.17813617 2.8956099 2.9411416
## 6 -1.01793981 0.2817057 2.228023 -0.86494124 -0.9747949 -0.1569053
## stat65 stat66 stat67 stat68 stat69 stat70
## 1 -2.9490898 -0.3325469 1.5745990 -2.2978280 1.5451891 -1.345990
## 2 -1.1174885 -1.5728682 -2.9229002 0.2658547 -1.9616533 2.506130
## 3 -0.2231264 -0.4503301 0.7932286 -1.2453773 -2.2309763 2.309761
## 4 -0.3522418 -2.0720532 0.9442933 2.9212906 0.5100371 -2.441108
## 5 -2.1648991 1.2002029 2.8266985 0.7461294 1.6772674 -1.280000
## 6 -2.2295458 1.1446493 0.2024925 -0.2983998 -2.8203752 1.224030
## stat71 stat72 stat73 stat74 stat75 stat76
## 1 1.0260956 2.1071210 2.6625669 -2.8924677 -0.02132523 -2.5058765
## 2 0.3525076 1.6922342 -1.2167022 -1.7271879 2.21176434 1.9329683
## 3 -2.1799035 -2.2645276 0.1415582 0.9887453 1.95592320 0.2951785
## 4 -2.4051409 2.0876484 -0.8632146 0.4011389 -1.16986716 -1.2391174
## 5 1.3538754 -0.8089395 -0.5122626 -2.1696892 1.07344925 2.6696169
## 6 -2.8073371 -1.4450488 0.5481212 -1.4381690 0.80917043 -0.1365944
## stat77 stat78 stat79 stat80 stat81 stat82
## 1 -2.5631845 -2.40331340 0.38416120 -1.2564875 -0.1550840 -1.1762617
## 2 -0.4462085 0.38400793 1.80483031 -0.8387642 0.7624431 0.9936900
## 3 1.6757870 -1.81900752 2.70904708 -0.3201959 2.5754235 1.6346260
## 4 -2.1012006 -2.24691081 1.78056848 1.0323739 1.0762523 2.1343851
## 5 -2.5736733 -1.99958372 -0.05388495 -2.5630073 -2.8783002 -0.5752426
## 6 1.6143972 0.03233746 2.90835762 1.4000487 2.9275615 -2.8503830
## stat83 stat84 stat85 stat86 stat87 stat88
## 1 1.2840565 -2.6794965 1.3956039 -1.5290235 2.221152 2.3794982
## 2 -0.2380048 1.9314318 -1.6747955 -0.3663656 1.582659 -0.5222489
## 3 -0.9150769 -1.5520337 2.4186287 2.7273662 1.306642 0.1320062
## 4 -2.5824408 -2.7775943 0.5085060 0.4689015 2.053348 0.7957955
## 5 -1.0017741 -0.2009138 0.3770109 2.4335201 -1.118058 1.3953410
## 6 2.4891765 2.9931953 -1.4171852 0.3905659 -1.856119 -2.1690490
## stat89 stat90 stat91 stat92 stat93 stat94
## 1 -0.9885110 -0.8873261 -2.7810929 -1.53325891 2.6002395 1.8890998
## 2 0.9982028 -1.2382015 -0.1574496 0.41086048 -0.5412626 -0.2421387
## 3 0.5956759 1.6871066 2.2452753 2.74279594 -1.5860478 2.9393122
## 4 2.0902634 2.1752586 -2.0677712 -2.37861037 1.1653302 0.1500632
## 5 2.9820614 0.8111660 -0.7842287 0.03766387 -1.1681970 2.1217251
## 6 -1.7428021 0.1579032 1.7456742 -0.36858466 -0.1304616 -1.4555819
## stat95 stat96 stat97 stat98 stat99 stat100
## 1 -2.6056035 -0.5814857 2.57652426 -2.3297751 2.6324007 1.445827
## 2 -2.0271583 -0.9126074 2.49582648 0.9745382 1.1339203 -2.549544
## 3 0.3823181 -0.6324139 2.46221566 1.1151560 0.4624891 0.107072
## 4 2.6414623 -0.6630505 2.10394859 1.2627635 0.4861740 1.697012
## 5 1.4642254 2.6485956 -0.07699547 0.6219473 -1.8815142 -2.685463
## 6 1.8937331 -0.4690555 1.04671776 -0.5879866 -0.9766789 2.405940
## stat101 stat102 stat103 stat104 stat105 stat106
## 1 -2.1158021 2.603936 1.7745128 -1.8903574 -1.8558655 1.0122044
## 2 -2.7998588 -2.267895 0.5336456 -0.2859477 -0.5196246 -0.9417582
## 3 0.7969509 -1.744906 -0.7960327 1.9767258 -0.2007264 -0.7872376
## 4 1.7071959 -1.540221 1.6770362 1.5395796 -0.4855365 -1.2894115
## 5 -1.4627420 -1.700983 2.4376490 0.2731541 1.5275587 1.3256483
## 6 2.6888530 1.090155 2.0769854 1.9615480 1.8689761 2.8975825
## stat107 stat108 stat109 stat110 stat111 stat112
## 1 1.954508 -0.3376471 2.503084 0.3099165 2.7209847 -2.3911204
## 2 -2.515160 0.3998704 -1.077093 2.4228268 -0.7759693 0.2513882
## 3 1.888827 1.5819857 -2.066659 -2.0008364 0.6997684 2.6157095
## 4 1.076395 -1.8524148 -2.689204 1.0985872 1.2389493 2.1018629
## 5 2.828866 -1.8590252 -2.424163 1.4391942 -0.6173239 -1.5218846
## 6 -1.419639 0.7888914 1.996463 0.9813507 0.9034198 1.3810679
## stat113 stat114 stat115 stat116 stat117 stat118
## 1 -1.616161 1.0878664 0.9860094 -0.06288462 -1.013501 -1.2212842
## 2 -1.554771 1.8683100 0.4880588 -0.63865489 -1.610217 -1.7713343
## 3 -2.679801 -2.9486952 1.7753417 0.90311784 -1.318836 -0.1429040
## 4 2.459229 -0.5584171 0.4419581 -0.09586351 0.595442 0.2883342
## 5 -2.102200 1.6300170 -2.3498287 1.36771894 -1.912202 -0.2563821
## 6 -1.835037 0.6577786 -2.9928374 2.13540316 -1.437299 -0.9570006
## stat119 stat120 stat121 stat122 stat123 stat124
## 1 2.9222729 1.9151262 1.6686068 2.0061224 1.5723072 0.78819227
## 2 2.1828208 0.8283178 -2.4458632 1.7133740 1.1393738 -0.07182054
## 3 0.9721319 1.2723130 2.8002086 2.7670381 -2.2252586 2.17499113
## 4 -1.9327896 -2.5369370 1.7835028 1.0262097 -1.8790983 -0.43639564
## 5 1.3230809 -2.8145256 -0.9547533 -2.0435417 -0.2758764 -1.85668027
## 6 0.1720700 -1.4568460 1.4115051 -0.9878145 2.3895061 -2.33730745
## stat125 stat126 stat127 stat128 stat129 stat130
## 1 1.588372 1.1620011 -0.2474264 1.650328 2.5147598 0.37283245
## 2 -1.173771 0.8162020 0.3510315 -1.263667 1.7245284 -0.72852904
## 3 -1.503497 -0.5656394 2.8040256 -2.139287 -1.7221642 2.17899609
## 4 1.040967 -2.9039600 0.3103742 1.462339 -1.2940350 -2.95015502
## 5 -2.866184 1.6885070 -2.2525666 -2.628631 1.8581577 2.80127025
## 6 -1.355111 1.5017927 0.4295921 -0.580415 0.9851009 -0.03773117
## stat131 stat132 stat133 stat134 stat135 stat136
## 1 -0.09028241 0.5194538 2.8478346 2.6664724 -2.0206311 1.398415090
## 2 -0.53045595 1.4134049 2.9180586 0.3299096 1.4784122 -1.278896090
## 3 1.35843194 0.2279946 0.3532595 0.6138676 -0.3443284 0.057763811
## 4 -1.92450273 1.2698178 -1.5299660 -2.6083462 1.1665530 -0.187791914
## 5 1.49036849 2.6337729 -2.3206244 0.4978287 -1.7397571 0.001200184
## 6 -0.64642709 -1.9256228 1.7032650 -0.9152725 -0.3188055 2.155395980
## stat137 stat138 stat139 stat140 stat141 stat142
## 1 -1.2794871 0.4064890 -0.4539998 2.6660173 -1.8375313 0.4711883
## 2 -2.7709017 -1.6303773 -1.9025910 0.2572918 0.6612002 1.4764348
## 3 -1.1930757 -0.1051243 -0.5108380 -1.0879666 2.4969513 -0.9477230
## 4 -1.2318919 2.2348571 0.1788580 -1.5851788 -1.2384283 -2.1859181
## 5 1.8685058 2.7229517 -2.9077182 2.6606939 -1.5963592 -2.2213492
## 6 -0.4807318 -1.2117369 -0.9358531 -2.5100758 -2.3803916 -0.7096854
## stat143 stat144 stat145 stat146 stat147 stat148
## 1 1.9466263 2.2689433 -0.3597288 -0.6551386 1.65438592 0.6404466
## 2 1.3156421 2.4459090 -0.3790028 1.4858465 -0.07784461 1.0096149
## 3 0.1959563 2.3062942 1.8459278 2.6848175 -2.70935774 -1.2093409
## 4 1.7633296 -2.8171508 2.0902622 -2.6625464 -1.12600601 -2.1926479
## 5 0.3885758 1.8160636 2.8257299 -1.4526173 1.60679603 2.3807991
## 6 0.7623450 0.2692145 -2.4307463 -2.1244523 -2.67803812 -1.5273387
## stat149 stat150 stat151 stat152 stat153 stat154
## 1 0.1583575 0.4755351 0.3213410 2.0241520 1.5720103 -0.1825875
## 2 -0.4311406 2.9577663 0.6937252 0.1397280 0.3775735 -1.1012636
## 3 -0.8352824 2.5716205 1.7528236 0.4326277 -2.2334397 -2.6265771
## 4 -2.8069143 1.8813509 2.3358023 0.1015632 1.2117474 -1.3714278
## 5 -1.6166265 1.1112266 -1.1998471 2.9316769 -2.1676455 -0.3411089
## 6 -0.2265472 2.7264354 -1.6746094 -2.3376281 -1.7022788 -1.2352397
## stat155 stat156 stat157 stat158 stat159 stat160
## 1 -1.139657 0.07061254 0.5893906 -1.9920996 -2.83714366 2.249398
## 2 -2.041093 0.74047768 2.5415072 -1.2697256 -1.64364433 -2.448922
## 3 -1.219507 -0.55198693 0.4046920 1.2098547 -0.90412390 -1.934093
## 4 2.992191 2.33222485 2.0622969 -0.6714653 2.76836085 -1.431120
## 5 -2.362356 -1.23906672 0.4746319 -0.7849202 0.69399995 2.052411
## 6 -1.604499 1.31051409 -0.5164744 0.6288667 0.07899523 -2.287402
## stat161 stat162 stat163 stat164 stat165 stat166
## 1 1.7182635 -1.2323593 2.7350423 1.0707235 1.1621544 0.9493989
## 2 -0.6247674 2.6740098 2.8211024 1.5561292 -1.1027147 1.0519739
## 3 -0.6230453 -0.7993517 -2.8318374 -1.1148673 1.4261659 0.5294309
## 4 1.7644744 0.1696584 1.2653207 0.6621516 0.9470508 0.1985014
## 5 -1.2070210 0.7243784 0.9736322 2.7426259 -2.6862383 1.6840212
## 6 2.3705316 -2.1667893 -0.2516685 -0.8425958 -1.9099342 -2.8607297
## stat167 stat168 stat169 stat170 stat171 stat172
## 1 0.1146510 2.3872008 1.1180918 -0.95370555 -2.25076509 0.2348182
## 2 1.0760417 -2.0449336 0.9715676 -0.40173489 -0.11953555 -2.3107369
## 3 1.1735898 1.3860190 -2.2894719 0.06350347 0.29191551 -1.6079744
## 4 2.5511832 0.5446648 1.2694012 -0.84571201 0.79789722 0.2623538
## 5 2.2900002 2.6289782 -0.2783571 1.39032829 -0.55532032 1.0499046
## 6 -0.7513983 2.9617066 -2.2119520 -1.71958113 -0.01452018 -0.2751517
## stat173 stat174 stat175 stat176 stat177 stat178
## 1 1.79366076 -1.920206 -0.38841942 0.8530301 1.64532077 -1.1354179
## 2 -0.07484659 1.337846 2.20911694 0.9616837 -2.80810070 -2.1136749
## 3 -1.05521810 -1.483741 0.06148359 2.3066039 -0.34688616 1.1840581
## 4 0.31460321 1.195741 2.97633862 1.1685091 -0.06346265 1.4205489
## 5 -1.39428365 2.458523 0.64836472 -1.0396386 -0.57828104 -0.5006818
## 6 2.31844401 1.239864 -2.06490874 0.7696204 -1.77586019 2.0855925
## stat179 stat180 stat181 stat182 stat183 stat184
## 1 2.0018647 0.1476815 -1.27279520 1.9181504 -0.5297624 -2.9718938
## 2 -2.1351449 2.9012582 -1.09914911 -2.5488517 -2.8377736 1.4073374
## 3 -1.7819908 2.9902627 0.81908613 0.2503852 0.3712984 -2.1714024
## 4 -0.1026974 -2.4763253 -2.52645421 1.3096315 2.1458161 -1.5228094
## 5 -2.2298794 2.4465680 -0.70346898 -1.6997617 2.9178164 -0.3615532
## 6 -1.1168108 1.5552123 -0.01361342 1.7338791 -1.1104763 0.1882416
## stat185 stat186 stat187 stat188 stat189 stat190
## 1 -0.1043832 -1.5047463 2.700351 -2.4780862 -1.9078265 0.9978108
## 2 -2.0310574 -0.5380074 -1.963275 -1.2221278 -2.4290681 -1.9515115
## 3 2.6727278 1.2688179 -1.399018 -2.9612138 2.6456394 2.0073323
## 4 -2.7796295 2.0682354 2.243727 0.4296881 0.1931333 2.2710960
## 5 -0.6231265 2.5833981 2.229041 0.8139584 1.4544131 1.8886451
## 6 2.7204690 -2.4469144 -1.421998 1.7477882 -0.1481806 0.6011560
## stat191 stat192 stat193 stat194 stat195 stat196
## 1 -0.6644351 2.6270833 -1.1094601 -2.4200392 2.870713 -0.6590932
## 2 -0.6483142 1.4519118 -0.1963493 -2.3025322 1.255608 2.1617947
## 3 -1.5457382 -0.2977442 -1.7045015 0.7962404 -1.696063 -1.4771117
## 4 -1.1780495 -2.9747574 -1.1471518 -1.2377013 -1.010672 -2.6055975
## 5 2.8813178 -1.8964081 -1.2653487 -1.7839754 -2.872581 2.3033464
## 6 0.4437973 0.6599325 -1.4029555 -2.3118258 -1.792232 1.3934380
## stat197 stat198 stat199 stat200 stat201 stat202
## 1 -0.83056986 0.9550526 -1.7025776 -2.8263099 -0.7023998 0.2272806
## 2 -1.42178249 -1.2471864 2.5723093 -0.0233496 -1.8975239 1.9472262
## 3 -0.19233958 -0.5161456 0.0279946 -1.2333704 -2.9672263 -2.8666208
## 4 -1.23145902 1.4728470 -0.4562025 -2.2983441 -1.5101184 0.2530525
## 5 1.85018563 -1.8269292 -0.6337969 -2.1473246 0.9909850 1.0950903
## 6 -0.09311061 0.5144456 -2.8178268 -2.7555969 -2.3546004 -1.0558939
## stat203 stat204 stat205 stat206 stat207 stat208
## 1 1.166631220 0.007453276 2.9961641 1.5327307 -2.2293356 -0.9946009
## 2 -0.235396504 2.132749800 0.3707606 1.5604026 -1.0089217 2.1474257
## 3 0.003180946 2.229793310 2.7354040 0.8992231 2.9694967 2.3081024
## 4 -0.474482715 -1.584772230 -2.3224132 -0.9409741 -2.3179255 0.8032548
## 5 2.349412920 -1.276320220 -2.0203719 -1.1733509 1.0371852 -2.5086207
## 6 0.727436960 -0.960191786 -0.8964998 -1.6406623 -0.2330488 1.7993879
## stat209 stat210 stat211 stat212 stat213 stat214
## 1 -2.2182105 -1.4099774 -1.656754 2.6602585 -2.9270992 1.1240714
## 2 -2.8932488 -1.1641679 -2.605423 -1.5650513 2.9523673 2.0266318
## 3 -1.8279589 0.0472350 -2.026734 2.5054367 0.9903042 0.3274105
## 4 -1.0878067 0.1171303 2.645891 -1.6775225 1.3452160 1.4694063
## 5 -0.8158175 0.4060950 0.912256 0.2925677 2.1610141 0.5679936
## 6 -2.2664354 -0.2061083 -1.435174 2.6645632 0.4216259 -0.6419122
## stat215 stat216 stat217
## 1 -2.7510750 -0.5501796 1.2638469
## 2 2.8934650 -2.4099574 -1.2411407
## 3 -1.0947676 1.2852937 1.5411530
## 4 0.6343777 0.1345372 2.9102673
## 5 0.9908702 1.7909757 -2.0902610
## 6 -2.8113887 -1.0624912 0.2765074features = features.highprec
#str(features) Checking correlations to evaluate removal of redundant features
corr.matrix = round(cor(features[sapply(features, is.numeric)]),2)
# filter out only highly correlated variables
threshold = 0.6
corr.matrix.tmp = corr.matrix
diag(corr.matrix.tmp) = 0
high.corr = apply(abs(corr.matrix.tmp) >= threshold, 1, any)
high.corr.matrix = corr.matrix.tmp[high.corr, high.corr]
DT::datatable(corr.matrix)DT::datatable(high.corr.matrix)Feature Names
feature.names = colnames(features)
drops <- c('JobName')
feature.names = feature.names[!(feature.names %in% drops)]
#str(feature.names)Read and Clean Labels
labels = read.csv("../../Data/labels.csv")
#str(labels)
labels = labels[,c("JobName", output.var)]
summary(labels)## JobName y3
## Job_00001: 1 Min. : 95.91
## Job_00002: 1 1st Qu.:118.21
## Job_00003: 1 Median :123.99
## Job_00004: 1 Mean :125.36
## Job_00005: 1 3rd Qu.:131.06
## Job_00006: 1 Max. :193.73
## (Other) :9994 NA's :2497Merge Datasets
data <- merge(features, labels, by = 'JobName')
drops <- c('JobName')
data = data[,(!colnames(data) %in% drops)]
#str(data)Transformations
if (transform.abs == TRUE){
data[,label.names] = 10^(data[,label.names]/20)
#data = filter(data, y3 < 1E7)
}
if (log.pred == TRUE){
data[label.names] = log(data[alt.scale.label.name],10)
drops = c(alt.scale.label.name)
data = data[!(names(data) %in% drops)]
}
t = NULL # initializw to NULL for other cases
if (norm.pred){
t = bestNormalize::bestNormalize(data[[alt.scale.label.name]])
data[label.names] = predict(t)
drops = c(alt.scale.label.name)
data = data[!(names(data) %in% drops)]
}
#str(data)Remove NA Cases
data = data[complete.cases(data),]Exploratory Data Analysis
Check correlation of Label with Featires
if (eda == TRUE){
corr.to.label =round(cor(dplyr::select(data,-one_of(label.names)),dplyr::select_at(data,label.names)),4)
DT::datatable(corr.to.label)
}Multicollinearity - VIF
if (eda == TRUE){
vifDF = usdm::vif(select_at(data,feature.names)) %>% arrange(desc(VIF))
head(vifDF,10)
}Scatterplots
panel.hist <- function(x, ...)
{
usr <- par("usr"); on.exit(par(usr))
par(usr = c(usr[1:2], 0, 1.5) )
h <- hist(x, plot = FALSE)
breaks <- h$breaks; nB <- length(breaks)
y <- h$counts; y <- y/max(y)
rect(breaks[-nB], 0, breaks[-1], y, col = "cyan", ...)
}if (eda == TRUE){
histogram(data[ ,label.names])
#hist(data[complete.cases(data),alt.scale.label.name])
}# https://stackoverflow.com/questions/24648729/plot-one-numeric-variable-against-n-numeric-variables-in-n-plots
ind.pairs.plot <- function(data, xvars=NULL, yvar)
{
df <- data
if (is.null(xvars)) {
xvars = names(data[which(names(data)!=yvar)])
}
#choose a format to display charts
ncharts <- length(xvars)
for(i in 1:ncharts){
plot(df[,xvars[i]],df[,yvar], xlab = xvars[i], ylab = yvar)
}
}
if (eda == TRUE){
ind.pairs.plot(data, feature.names, label.names)
}
#
# pl <- ggplot(data, aes(x=x18, y = y3))
# pl2 <- pl + geom_point(aes(alpha = 0.1)) # default color gradient based on 'hp'
# print(pl2)Feature Engineering
if(eda ==FALSE){
# x18 may need transformations
plot(data[,'x18'], data[,label.names], main = "Original Scatter Plot vs. x18", ylab = label.names, xlab = 'x18')
plot(sqrt(data[,'x18']), data[,label.names], main = "Original Scatter Plot vs. sqrt(x18)", ylab = label.names, xlab = 'sqrt(x18)')
# transforming x18
data$sqrt.x18 = sqrt(data$x18)
data = dplyr::select(data,-one_of('x18'))
# what about x7, x9?
# x11 looks like data is at discrete points after a while. Will this be a problem?
}
Modeling
Train Test Split
data = data[sample(nrow(data)),] # randomly shuffle data
split = sample.split(data[,label.names], SplitRatio = 0.8)
data.train = subset(data, split == TRUE)
data.test = subset(data, split == FALSE)Common Functions
plot.diagnostics <- function(model, train) {
plot(model)
residuals = resid(model) # Plotted above in plot(lm.out)
r.standard = rstandard(model)
r.student = rstudent(model)
plot(predict(model,train),r.student,
ylab="Student Residuals", xlab="Predicted Values",
main="Student Residual Plot")
abline(0, 0)
plot(predict(model, train),r.standard,
ylab="Standard Residuals", xlab="Predicted Values",
main="Standard Residual Plot")
abline(0, 0)
abline(2, 0)
abline(-2, 0)
# Histogram
hist(r.student, freq=FALSE, main="Distribution of Studentized Residuals",
xlab="Studentized Residuals", ylab="Density", ylim=c(0,0.5))
# Create range of x-values for normal curve
xfit <- seq(min(r.student)-1, max(r.student)+1, length=40)
# Generate values from the normal distribution at the specified values
yfit <- (dnorm(xfit))
# Add the normal curve
lines(xfit, yfit, ylim=c(0,0.5))
# http://www.stat.columbia.edu/~martin/W2024/R7.pdf
# Influential plots
inf.meas = influence.measures(model)
# print (summary(inf.meas)) # too much data
# Leverage plot
lev = hat(model.matrix(model))
plot(lev, ylab = 'Leverage - check')
# Cook's Distance
cd = cooks.distance(model)
plot(cd,ylab="Cooks distances")
abline(4/nrow(train),0)
abline(1,0)
print (paste("Number of data points that have Cook's D > 4/n: ", length(cd[cd > 4/nrow(train)]), sep = ""))
print (paste("Number of data points that have Cook's D > 1: ", length(cd[cd > 1]), sep = ""))
return(cd)
}
train.caret.glmselect = function(formula, data, method
,subopt = NULL, feature.names
, train.control = NULL, tune.grid = NULL, pre.proc = NULL){
if(is.null(train.control)){
train.control <- trainControl(method = "cv"
,number = 10
,search = "grid"
,verboseIter = TRUE
,allowParallel = TRUE
)
}
if(is.null(tune.grid)){
if (method == 'leapForward' | method == 'leapBackward' | method == 'leapSeq'){
tune.grid = data.frame(nvmax = 1:length(feature.names))
}
if (method == 'glmnet' && subopt == 'LASSO'){
# Will only show 1 Lambda value during training, but that is OK
# https://stackoverflow.com/questions/47526544/why-need-to-tune-lambda-with-carettrain-method-glmnet-and-cv-glmnet
# Another option for LASSO is this: https://github.com/topepo/caret/blob/master/RegressionTests/Code/lasso.R
lambda = 10^seq(-2,0, length =100)
alpha = c(1)
tune.grid = expand.grid(alpha = alpha,lambda = lambda)
}
if (method == 'lars'){
# https://github.com/topepo/caret/blob/master/RegressionTests/Code/lars.R
fraction = seq(0, 1, length = 100)
tune.grid = expand.grid(fraction = fraction)
pre.proc = c("center", "scale")
}
}
# http://sshaikh.org/2015/05/06/parallelize-machine-learning-in-r-with-multi-core-cpus/
cl <- makeCluster(detectCores()*0.75) # use 75% of cores only, leave rest for other tasks
registerDoParallel(cl)
set.seed(1)
# note that the seed has to actually be set just before this function is called
# settign is above just not ensure reproducibility for some reason
model.caret <- caret::train(formula
, data = data
, method = method
, tuneGrid = tune.grid
, trControl = train.control
, preProc = pre.proc
)
stopCluster(cl)
registerDoSEQ() # register sequential engine in case you are not using this function anymore
if (method == 'leapForward' | method == 'leapBackward' | method == 'leapSeq'){
print(model.caret$results) # all model results
print(model.caret$bestTune) # best model
model = model.caret$finalModel
# Metrics Plot
dataPlot = model.caret$results %>%
gather(key='metric',value='value',-nvmax) %>%
dplyr::filter(metric %in% c('MAE','RMSE','Rsquared'))
metricsPlot = ggplot(data=dataPlot,aes(x=nvmax,y=value) ) +
geom_line(color='lightblue4') +
geom_point(color='blue',alpha=0.7,size=.9) +
facet_wrap(~metric,ncol=4,scales='free_y')+
theme_light()
plot(metricsPlot)
# Residuals Plot
# leap function does not support studentized residuals
dataPlot=data.frame(pred=predict(model.caret,data),res=resid(model.caret))
residPlot = ggplot(dataPlot,aes(x=pred,y=res)) +
geom_point(color='light blue',alpha=0.7) +
geom_smooth()+
theme_light()
plot(residPlot)
residHistogram = ggplot(dataPlot,aes(x=res)) +
geom_histogram(aes(y=..density..),fill='light blue',alpha=1) +
geom_density(color='lightblue4') +
theme_light()
plot(residHistogram)
# Provides the coefficients of the best model
id = rownames(model.caret$bestTune)
message("Coefficients of final model:")
print (coef(model, id = id))
return(list(model = model,id = id, residPlot = residPlot, residHistogram=residHistogram))
}
if (method == 'glmnet' && subopt == 'LASSO'){
print(model.caret)
print(plot(model.caret))
print(model.caret$bestTune)
print(model.caret$results)
# Metrics Plot
dataPlot = model.caret$results %>%
gather(key='metric',value='value',-lambda) %>%
dplyr::filter(metric %in% c('MAE','RMSE','Rsquared'))
metricsPlot = ggplot(data=dataPlot,aes(x=lambda,y=value) ) +
geom_line(color='lightblue4') +
geom_point(color='blue',alpha=0.7,size=.9) +
facet_wrap(~metric,ncol=4,scales='free_y')+
theme_light()
plot(metricsPlot)
# Residuals Plot
dataPlot=data.frame(pred=predict(model.caret,data),res=resid(model.caret))
residPlot = ggplot(dataPlot,aes(x=pred,y=res)) +
geom_point(color='light blue',alpha=0.7) +
geom_smooth()+
theme_light()
plot(residPlot)
residHistogram = ggplot(dataPlot,aes(x=res)) +
geom_histogram(aes(y=..density..),fill='light blue',alpha=1) +
geom_density(color='lightblue4') +
theme_light()
plot(residHistogram)
id = NULL # not really needed but added for consistency
return(list(model = model.caret,id = id, residPlot = residPlot, metricsPlot=metricsPlot ))
}
if (method == 'lars'){
print(model.caret)
print(plot(model.caret))
print(model.caret$bestTune)
# Metrics Plot
dataPlot = model.caret$results %>%
gather(key='metric',value='value',-fraction) %>%
dplyr::filter(metric %in% c('MAE','RMSE','Rsquared'))
metricsPlot = ggplot(data=dataPlot,aes(x=fraction,y=value) ) +
geom_line(color='lightblue4') +
geom_point(color='blue',alpha=0.7,size=.9) +
facet_wrap(~metric,ncol=4,scales='free_y')+
theme_light()
plot(metricsPlot)
# Residuals Plot
dataPlot=data.frame(pred=predict(model.caret,data),res=resid(model.caret))
residPlot = ggplot(dataPlot,aes(x=pred,y=res)) +
geom_point(color='light blue',alpha=0.7) +
geom_smooth()+
theme_light()
plot(residPlot)
residHistogram = ggplot(dataPlot,aes(x=res)) +
geom_histogram(aes(y=..density..),fill='light blue',alpha=1) +
geom_density(color='lightblue4') +
theme_light()
plot(residHistogram)
id = NULL # not really needed but added for consistency
return(list(model = model.caret,id = id, residPlot = residPlot, residHistogram=residHistogram))
}
}
# https://stackoverflow.com/questions/48265743/linear-model-subset-selection-goodness-of-fit-with-k-fold-cross-validation
# changed slightly since call[[2]] was just returning "formula" without actually returnign the value in formula
predict.regsubsets <- function(object, newdata, id, formula, ...) {
#form <- as.formula(object$call[[2]])
mat <- model.matrix(formula, newdata) # adds intercept and expands any interaction terms
coefi <- coef(object, id = id)
xvars <- names(coefi)
return(mat[,xvars]%*%coefi)
}
test.model = function(model, test, level=0.95
,draw.limits = FALSE, good = 0.1, ok = 0.15
,method = NULL, subopt = NULL
,id = NULL, formula, feature.names, label.names
,transformation = NULL){
## if using caret for glm select equivalent functionality,
## need to pass formula (full is ok as it will select subset of variables from there)
if (is.null(method)){
pred = predict(model, newdata=test, interval="confidence", level = level)
}
if (method == 'leapForward' | method == 'leapBackward' | method == 'leapSeq'){
pred = predict.regsubsets(model, newdata = test, id = id, formula = formula)
}
if (method == 'glmnet' && subopt == 'LASSO'){
xtest = as.matrix(test[,feature.names])
pred=as.data.frame(predict(model, xtest))
}
if (method == 'lars'){
pred=as.data.frame(predict(model, newdata = test))
}
# Summary of predicted values
print ("Summary of predicted values: ")
print(summary(pred[,1]))
test.mse = mean((test[,label.names]-pred[,1])^2)
print (paste(method, subopt, "Test MSE:", test.mse, sep=" "))
if(log.pred == TRUE || norm.pred == TRUE){
# plot transformewd comparison first
plot(test[,label.names],pred[,1],xlab = "Actual (Transformed)", ylab = "Predicted (Transformed)")
}
if (log.pred == FALSE && norm.pred == FALSE){
x = test[,label.names]
y = pred[,1]
}
if (log.pred == TRUE){
x = 10^test[,label.names]
y = 10^pred[,1]
}
if (norm.pred == TRUE){
x = predict(transformation, test[,label.names], inverse = TRUE)
y = predict(transformation, pred[,1], inverse = TRUE)
}
plot(x, y, xlab = "Actual", ylab = "Predicted")
abline(0,(1+good),col='green', lwd = 3)
abline(0,(1-good),col='green', lwd = 3)
abline(0,(1+ok),col='blue', lwd = 3)
abline(0,(1-ok),col='blue', lwd = 3)
}Setup Formulae
n <- names(data.train)
formula <- as.formula(paste(paste(n[n %in% label.names], collapse = " + ")," ~", paste(n[!n %in% label.names], collapse = " + ")))
# ind.interact = c("x4","x7","x8", "x9", "x10", "x11", "x14", "x16", "x17", "x21", "sqrt.x18")
# ind.nointeract = c("stat13", "stat14", "stat24", "stat60", "stat98", "stat110", "stat144", "stat149")
#
# interact = paste(ind.interact, collapse = " + ")
# nointeract = paste(ind.nointeract, collapse = " + ")
#
# # ^2 is 2 way interaction, ^3 is 3 way interaction
# formula <- as.formula(paste(paste(n[n %in% label.names], collapse = " + "), "~ (", interact, " )^2 ", " + ", nointeract ))
#
# # # * is all way interaction
# # formula <- as.formula(paste(paste(n[n %in% label.names], collapse = " + "), "~ (", interact, " ) ", " + ", nointeract ))
grand.mean.formula = as.formula(paste(paste(n[n %in% label.names], collapse = " + ")," ~ 1"))
print(formula)## log.y3 ~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9 + x10 + x11 +
## x12 + x13 + x14 + x15 + x16 + x17 + x19 + x20 + x21 + x22 +
## x23 + stat1 + stat2 + stat3 + stat4 + stat5 + stat6 + stat7 +
## stat8 + stat9 + stat10 + stat11 + stat12 + stat13 + stat14 +
## stat15 + stat16 + stat17 + stat18 + stat19 + stat20 + stat21 +
## stat22 + stat23 + stat24 + stat25 + stat26 + stat27 + stat28 +
## stat29 + stat30 + stat31 + stat32 + stat33 + stat34 + stat35 +
## stat36 + stat37 + stat38 + stat39 + stat40 + stat41 + stat42 +
## stat43 + stat44 + stat45 + stat46 + stat47 + stat48 + stat49 +
## stat50 + stat51 + stat52 + stat53 + stat54 + stat55 + stat56 +
## stat57 + stat58 + stat59 + stat60 + stat61 + stat62 + stat63 +
## stat64 + stat65 + stat66 + stat67 + stat68 + stat69 + stat70 +
## stat71 + stat72 + stat73 + stat74 + stat75 + stat76 + stat77 +
## stat78 + stat79 + stat80 + stat81 + stat82 + stat83 + stat84 +
## stat85 + stat86 + stat87 + stat88 + stat89 + stat90 + stat91 +
## stat92 + stat93 + stat94 + stat95 + stat96 + stat97 + stat98 +
## stat99 + stat100 + stat101 + stat102 + stat103 + stat104 +
## stat105 + stat106 + stat107 + stat108 + stat109 + stat110 +
## stat111 + stat112 + stat113 + stat114 + stat115 + stat116 +
## stat117 + stat118 + stat119 + stat120 + stat121 + stat122 +
## stat123 + stat124 + stat125 + stat126 + stat127 + stat128 +
## stat129 + stat130 + stat131 + stat132 + stat133 + stat134 +
## stat135 + stat136 + stat137 + stat138 + stat139 + stat140 +
## stat141 + stat142 + stat143 + stat144 + stat145 + stat146 +
## stat147 + stat148 + stat149 + stat150 + stat151 + stat152 +
## stat153 + stat154 + stat155 + stat156 + stat157 + stat158 +
## stat159 + stat160 + stat161 + stat162 + stat163 + stat164 +
## stat165 + stat166 + stat167 + stat168 + stat169 + stat170 +
## stat171 + stat172 + stat173 + stat174 + stat175 + stat176 +
## stat177 + stat178 + stat179 + stat180 + stat181 + stat182 +
## stat183 + stat184 + stat185 + stat186 + stat187 + stat188 +
## stat189 + stat190 + stat191 + stat192 + stat193 + stat194 +
## stat195 + stat196 + stat197 + stat198 + stat199 + stat200 +
## stat201 + stat202 + stat203 + stat204 + stat205 + stat206 +
## stat207 + stat208 + stat209 + stat210 + stat211 + stat212 +
## stat213 + stat214 + stat215 + stat216 + stat217 + sqrt.x18print(grand.mean.formula)## log.y3 ~ 1# Update feature.names because we may have transformed some features
feature.names = n[!n %in% label.names]Full Model
model.full = lm(formula , data.train)
summary(model.full)##
## Call:
## lm(formula = formula, data = data.train)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.080692 -0.020501 -0.004585 0.016473 0.186465
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.969e+00 9.194e-03 214.199 < 2e-16 ***
## x1 -3.714e-04 6.308e-04 -0.589 0.556016
## x2 2.029e-04 4.033e-04 0.503 0.614904
## x3 6.598e-05 1.104e-04 0.598 0.550025
## x4 -4.635e-05 8.694e-06 -5.331 1.01e-07 ***
## x5 4.189e-04 2.854e-04 1.468 0.142244
## x6 -2.039e-04 5.787e-04 -0.352 0.724562
## x7 1.135e-02 6.123e-04 18.539 < 2e-16 ***
## x8 4.744e-04 1.435e-04 3.306 0.000952 ***
## x9 3.509e-03 3.192e-04 10.994 < 2e-16 ***
## x10 1.151e-03 2.981e-04 3.861 0.000114 ***
## x11 1.923e+05 7.145e+04 2.691 0.007134 **
## x12 -2.364e-04 1.822e-04 -1.298 0.194401
## x13 8.768e-05 7.255e-05 1.208 0.226915
## x14 -5.978e-04 3.117e-04 -1.918 0.055222 .
## x15 2.038e-04 2.974e-04 0.685 0.493261
## x16 7.069e-04 2.063e-04 3.426 0.000617 ***
## x17 1.501e-03 3.129e-04 4.797 1.65e-06 ***
## x19 2.802e-04 1.588e-04 1.764 0.077810 .
## x20 -8.767e-04 1.107e-03 -0.792 0.428400
## x21 1.448e-04 4.069e-05 3.558 0.000377 ***
## x22 -6.905e-04 3.335e-04 -2.070 0.038458 *
## x23 -2.608e-04 3.162e-04 -0.825 0.409534
## stat1 -4.274e-05 2.398e-04 -0.178 0.858540
## stat2 9.978e-06 2.380e-04 0.042 0.966553
## stat3 3.438e-04 2.405e-04 1.430 0.152833
## stat4 -4.653e-04 2.409e-04 -1.931 0.053472 .
## stat5 -4.413e-05 2.403e-04 -0.184 0.854277
## stat6 -3.359e-04 2.391e-04 -1.404 0.160225
## stat7 -3.452e-05 2.400e-04 -0.144 0.885629
## stat8 -1.619e-04 2.396e-04 -0.676 0.499300
## stat9 1.438e-04 2.394e-04 0.601 0.548151
## stat10 -2.989e-04 2.392e-04 -1.250 0.211444
## stat11 -1.660e-05 2.414e-04 -0.069 0.945179
## stat12 1.843e-04 2.382e-04 0.774 0.439251
## stat13 -4.257e-04 2.380e-04 -1.788 0.073766 .
## stat14 -7.296e-04 2.377e-04 -3.070 0.002152 **
## stat15 -2.308e-04 2.378e-04 -0.971 0.331726
## stat16 -6.308e-05 2.379e-04 -0.265 0.790903
## stat17 -1.660e-04 2.362e-04 -0.703 0.482233
## stat18 -2.890e-04 2.369e-04 -1.220 0.222689
## stat19 2.705e-04 2.398e-04 1.128 0.259296
## stat20 -3.758e-04 2.376e-04 -1.581 0.113844
## stat21 3.509e-05 2.398e-04 0.146 0.883665
## stat22 -4.594e-04 2.406e-04 -1.909 0.056271 .
## stat23 7.172e-04 2.382e-04 3.011 0.002612 **
## stat24 -3.358e-04 2.385e-04 -1.408 0.159248
## stat25 -3.531e-04 2.378e-04 -1.485 0.137662
## stat26 -1.834e-04 2.393e-04 -0.766 0.443639
## stat27 2.227e-04 2.395e-04 0.930 0.352520
## stat28 3.005e-05 2.393e-04 0.126 0.900059
## stat29 -1.420e-05 2.413e-04 -0.059 0.953085
## stat30 1.690e-04 2.412e-04 0.701 0.483525
## stat31 -9.686e-05 2.414e-04 -0.401 0.688250
## stat32 7.413e-05 2.398e-04 0.309 0.757283
## stat33 -2.770e-04 2.390e-04 -1.159 0.246600
## stat34 2.465e-04 2.386e-04 1.033 0.301578
## stat35 -4.587e-04 2.393e-04 -1.917 0.055306 .
## stat36 -3.615e-05 2.372e-04 -0.152 0.878856
## stat37 -1.976e-04 2.418e-04 -0.817 0.414032
## stat38 3.317e-04 2.398e-04 1.383 0.166685
## stat39 -3.836e-04 2.377e-04 -1.614 0.106651
## stat40 -3.543e-07 2.395e-04 -0.001 0.998819
## stat41 -1.833e-04 2.382e-04 -0.770 0.441490
## stat42 -5.272e-04 2.388e-04 -2.208 0.027283 *
## stat43 -3.186e-04 2.414e-04 -1.320 0.186983
## stat44 1.813e-04 2.396e-04 0.757 0.449135
## stat45 -5.480e-04 2.384e-04 -2.299 0.021559 *
## stat46 2.465e-04 2.394e-04 1.030 0.303078
## stat47 3.648e-05 2.411e-04 0.151 0.879742
## stat48 3.071e-04 2.384e-04 1.288 0.197804
## stat49 2.038e-04 2.377e-04 0.857 0.391442
## stat50 2.018e-04 2.382e-04 0.847 0.396940
## stat51 2.801e-04 2.381e-04 1.176 0.239570
## stat52 -1.407e-04 2.395e-04 -0.587 0.557058
## stat53 -1.050e-04 2.412e-04 -0.435 0.663516
## stat54 -5.186e-04 2.407e-04 -2.155 0.031225 *
## stat55 3.869e-04 2.366e-04 1.635 0.102017
## stat56 -2.670e-04 2.382e-04 -1.121 0.262241
## stat57 1.740e-04 2.380e-04 0.731 0.464909
## stat58 -1.002e-04 2.372e-04 -0.423 0.672600
## stat59 2.894e-04 2.387e-04 1.213 0.225290
## stat60 6.384e-04 2.402e-04 2.657 0.007900 **
## stat61 8.127e-06 2.387e-04 0.034 0.972838
## stat62 -2.989e-04 2.395e-04 -1.248 0.212096
## stat63 1.208e-04 2.389e-04 0.506 0.613000
## stat64 -1.151e-04 2.384e-04 -0.483 0.629287
## stat65 -2.970e-04 2.402e-04 -1.237 0.216252
## stat66 9.670e-05 2.410e-04 0.401 0.688301
## stat67 3.277e-05 2.400e-04 0.137 0.891418
## stat68 -9.402e-05 2.399e-04 -0.392 0.695181
## stat69 -3.027e-05 2.391e-04 -0.127 0.899267
## stat70 1.742e-04 2.379e-04 0.732 0.463981
## stat71 1.255e-04 2.374e-04 0.529 0.596903
## stat72 3.614e-04 2.419e-04 1.494 0.135221
## stat73 3.937e-05 2.397e-04 0.164 0.869544
## stat74 -5.566e-06 2.402e-04 -0.023 0.981511
## stat75 -6.287e-05 2.408e-04 -0.261 0.794025
## stat76 -5.627e-06 2.401e-04 -0.023 0.981304
## stat77 -9.281e-05 2.401e-04 -0.386 0.699167
## stat78 -1.718e-04 2.402e-04 -0.715 0.474502
## stat79 -1.057e-04 2.388e-04 -0.442 0.658158
## stat80 2.677e-04 2.396e-04 1.117 0.263915
## stat81 3.148e-04 2.389e-04 1.317 0.187726
## stat82 7.126e-05 2.393e-04 0.298 0.765899
## stat83 4.664e-05 2.388e-04 0.195 0.845138
## stat84 -3.076e-05 2.385e-04 -0.129 0.897386
## stat85 -5.686e-05 2.395e-04 -0.237 0.812350
## stat86 2.014e-04 2.397e-04 0.840 0.400779
## stat87 -2.418e-04 2.402e-04 -1.007 0.314148
## stat88 -1.391e-04 2.368e-04 -0.587 0.557010
## stat89 -3.123e-04 2.389e-04 -1.308 0.191093
## stat90 -3.623e-04 2.392e-04 -1.514 0.129965
## stat91 -3.167e-04 2.368e-04 -1.337 0.181168
## stat92 -2.580e-04 2.387e-04 -1.081 0.279858
## stat93 -2.738e-05 2.418e-04 -0.113 0.909844
## stat94 -1.426e-04 2.392e-04 -0.596 0.551079
## stat95 -1.040e-05 2.384e-04 -0.044 0.965200
## stat96 -1.130e-04 2.390e-04 -0.473 0.636324
## stat97 4.083e-05 2.372e-04 0.172 0.863318
## stat98 3.480e-03 2.360e-04 14.746 < 2e-16 ***
## stat99 4.343e-04 2.411e-04 1.801 0.071786 .
## stat100 4.028e-04 2.397e-04 1.680 0.092949 .
## stat101 -1.607e-04 2.403e-04 -0.669 0.503611
## stat102 -2.073e-06 2.389e-04 -0.009 0.993075
## stat103 -1.961e-04 2.431e-04 -0.807 0.419927
## stat104 -4.996e-04 2.372e-04 -2.107 0.035179 *
## stat105 3.968e-04 2.371e-04 1.674 0.094244 .
## stat106 -2.467e-04 2.378e-04 -1.038 0.299498
## stat107 -1.683e-04 2.387e-04 -0.705 0.480981
## stat108 -1.629e-04 2.394e-04 -0.680 0.496271
## stat109 1.383e-04 2.392e-04 0.578 0.563092
## stat110 -3.296e-03 2.372e-04 -13.897 < 2e-16 ***
## stat111 -1.197e-04 2.389e-04 -0.501 0.616463
## stat112 -3.948e-05 2.405e-04 -0.164 0.869622
## stat113 -2.554e-04 2.411e-04 -1.059 0.289476
## stat114 3.010e-04 2.392e-04 1.258 0.208333
## stat115 4.888e-05 2.375e-04 0.206 0.836974
## stat116 3.341e-04 2.402e-04 1.391 0.164309
## stat117 2.022e-04 2.397e-04 0.844 0.398908
## stat118 -9.247e-05 2.383e-04 -0.388 0.697998
## stat119 1.976e-05 2.402e-04 0.082 0.934456
## stat120 8.615e-05 2.366e-04 0.364 0.715777
## stat121 -6.578e-05 2.395e-04 -0.275 0.783563
## stat122 9.975e-05 2.388e-04 0.418 0.676112
## stat123 6.077e-05 2.427e-04 0.250 0.802310
## stat124 -1.801e-04 2.387e-04 -0.755 0.450569
## stat125 4.136e-04 2.409e-04 1.717 0.086033 .
## stat126 4.641e-04 2.383e-04 1.948 0.051523 .
## stat127 -7.579e-05 2.392e-04 -0.317 0.751354
## stat128 -1.959e-04 2.392e-04 -0.819 0.412671
## stat129 -1.552e-04 2.378e-04 -0.653 0.514053
## stat130 1.256e-04 2.397e-04 0.524 0.600360
## stat131 1.257e-04 2.398e-04 0.524 0.600099
## stat132 -1.612e-04 2.385e-04 -0.676 0.499162
## stat133 3.007e-05 2.386e-04 0.126 0.899704
## stat134 -2.965e-05 2.374e-04 -0.125 0.900591
## stat135 -5.505e-06 2.402e-04 -0.023 0.981720
## stat136 1.184e-04 2.401e-04 0.493 0.621768
## stat137 2.411e-04 2.379e-04 1.013 0.311015
## stat138 1.097e-04 2.390e-04 0.459 0.646207
## stat139 2.800e-04 2.401e-04 1.166 0.243552
## stat140 -2.943e-05 2.380e-04 -0.124 0.901587
## stat141 2.259e-04 2.364e-04 0.956 0.339351
## stat142 -8.146e-05 2.417e-04 -0.337 0.736150
## stat143 2.374e-04 2.401e-04 0.989 0.322837
## stat144 6.343e-04 2.382e-04 2.663 0.007778 **
## stat145 1.908e-04 2.417e-04 0.789 0.429927
## stat146 -4.891e-04 2.409e-04 -2.030 0.042401 *
## stat147 -4.230e-04 2.405e-04 -1.758 0.078745 .
## stat148 -2.812e-04 2.356e-04 -1.193 0.232806
## stat149 -6.638e-04 2.407e-04 -2.758 0.005827 **
## stat150 -1.105e-05 2.393e-04 -0.046 0.963184
## stat151 -2.253e-04 2.413e-04 -0.934 0.350510
## stat152 -1.677e-04 2.375e-04 -0.706 0.480281
## stat153 1.400e-04 2.419e-04 0.579 0.562619
## stat154 7.415e-05 2.416e-04 0.307 0.758932
## stat155 1.428e-04 2.390e-04 0.597 0.550345
## stat156 4.927e-04 2.401e-04 2.052 0.040173 *
## stat157 -7.008e-06 2.382e-04 -0.029 0.976530
## stat158 1.138e-04 2.424e-04 0.469 0.638788
## stat159 -1.946e-04 2.383e-04 -0.817 0.414089
## stat160 1.364e-04 2.401e-04 0.568 0.569945
## stat161 1.971e-04 2.406e-04 0.819 0.412732
## stat162 5.058e-05 2.360e-04 0.214 0.830329
## stat163 5.641e-07 2.411e-04 0.002 0.998134
## stat164 1.687e-04 2.400e-04 0.703 0.482032
## stat165 -9.582e-05 2.378e-04 -0.403 0.687052
## stat166 -2.864e-04 2.371e-04 -1.208 0.227059
## stat167 -2.036e-04 2.388e-04 -0.852 0.394040
## stat168 -9.854e-05 2.389e-04 -0.412 0.679991
## stat169 1.478e-04 2.393e-04 0.617 0.537021
## stat170 -2.387e-04 2.383e-04 -1.002 0.316558
## stat171 3.222e-04 2.398e-04 1.344 0.179110
## stat172 3.024e-04 2.381e-04 1.270 0.204035
## stat173 -3.604e-04 2.405e-04 -1.499 0.134003
## stat174 -2.338e-04 2.391e-04 -0.978 0.328155
## stat175 -2.978e-04 2.401e-04 -1.240 0.214960
## stat176 -3.232e-05 2.391e-04 -0.135 0.892508
## stat177 -1.045e-04 2.399e-04 -0.436 0.663116
## stat178 1.604e-04 2.414e-04 0.664 0.506402
## stat179 1.380e-04 2.385e-04 0.579 0.562875
## stat180 6.324e-06 2.374e-04 0.027 0.978747
## stat181 1.074e-04 2.410e-04 0.446 0.655848
## stat182 1.322e-04 2.396e-04 0.552 0.581064
## stat183 2.643e-04 2.380e-04 1.110 0.266876
## stat184 7.350e-05 2.406e-04 0.305 0.760048
## stat185 -2.387e-05 2.378e-04 -0.100 0.920053
## stat186 -1.529e-04 2.404e-04 -0.636 0.524697
## stat187 -6.713e-04 2.388e-04 -2.811 0.004950 **
## stat188 2.059e-04 2.380e-04 0.865 0.387006
## stat189 -2.701e-05 2.399e-04 -0.113 0.910369
## stat190 1.627e-04 2.378e-04 0.684 0.493914
## stat191 -3.461e-04 2.391e-04 -1.447 0.147879
## stat192 -2.573e-05 2.422e-04 -0.106 0.915410
## stat193 -1.104e-04 2.420e-04 -0.456 0.648396
## stat194 -7.400e-05 2.389e-04 -0.310 0.756712
## stat195 1.855e-04 2.396e-04 0.774 0.438849
## stat196 4.512e-05 2.426e-04 0.186 0.852446
## stat197 3.224e-04 2.374e-04 1.358 0.174531
## stat198 -5.409e-04 2.392e-04 -2.261 0.023803 *
## stat199 3.267e-04 2.371e-04 1.378 0.168395
## stat200 -2.239e-04 2.364e-04 -0.947 0.343750
## stat201 -4.231e-05 2.385e-04 -0.177 0.859202
## stat202 -2.507e-04 2.417e-04 -1.037 0.299732
## stat203 3.672e-05 2.380e-04 0.154 0.877421
## stat204 -5.177e-04 2.374e-04 -2.181 0.029255 *
## stat205 -2.181e-04 2.379e-04 -0.917 0.359306
## stat206 6.729e-05 2.404e-04 0.280 0.779602
## stat207 3.420e-04 2.383e-04 1.436 0.151175
## stat208 2.302e-04 2.404e-04 0.958 0.338349
## stat209 -3.134e-04 2.392e-04 -1.310 0.190098
## stat210 3.966e-06 2.392e-04 0.017 0.986774
## stat211 -1.592e-04 2.386e-04 -0.667 0.504808
## stat212 -9.267e-05 2.405e-04 -0.385 0.700019
## stat213 -1.623e-04 2.408e-04 -0.674 0.500358
## stat214 -5.173e-04 2.402e-04 -2.154 0.031275 *
## stat215 -2.403e-04 2.395e-04 -1.003 0.315689
## stat216 -2.614e-04 2.394e-04 -1.092 0.274931
## stat217 3.122e-04 2.399e-04 1.302 0.193114
## sqrt.x18 2.675e-02 9.130e-04 29.297 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03145 on 5761 degrees of freedom
## Multiple R-squared: 0.2776, Adjusted R-squared: 0.2475
## F-statistic: 9.223 on 240 and 5761 DF, p-value: < 2.2e-16cd.full = plot.diagnostics(model.full, data.train)
## [1] "Number of data points that have Cook's D > 4/n: 294"
## [1] "Number of data points that have Cook's D > 1: 0"Checking with removal of high influence points
high.cd = names(cd.full[cd.full > 4/nrow(data.train)])
data.train2 = data.train[!(rownames(data.train)) %in% high.cd,]
model.full2 = lm(formula , data.train2)
summary(model.full2)##
## Call:
## lm(formula = formula, data = data.train2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.062903 -0.017492 -0.002541 0.016588 0.071587
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.956e+00 7.579e-03 258.025 < 2e-16 ***
## x1 -4.544e-04 5.204e-04 -0.873 0.382604
## x2 1.015e-04 3.322e-04 0.306 0.759916
## x3 4.849e-05 9.057e-05 0.535 0.592416
## x4 -5.051e-05 7.173e-06 -7.042 2.13e-12 ***
## x5 3.730e-04 2.346e-04 1.590 0.111906
## x6 -3.688e-04 4.763e-04 -0.774 0.438758
## x7 1.245e-02 5.044e-04 24.684 < 2e-16 ***
## x8 5.583e-04 1.183e-04 4.719 2.43e-06 ***
## x9 3.210e-03 2.622e-04 12.243 < 2e-16 ***
## x10 1.519e-03 2.454e-04 6.189 6.50e-10 ***
## x11 2.492e+05 5.893e+04 4.230 2.38e-05 ***
## x12 -1.158e-04 1.495e-04 -0.775 0.438487
## x13 1.156e-04 5.979e-05 1.934 0.053179 .
## x14 -4.011e-04 2.565e-04 -1.563 0.118033
## x15 2.916e-04 2.444e-04 1.193 0.232873
## x16 7.755e-04 1.700e-04 4.563 5.16e-06 ***
## x17 1.490e-03 2.570e-04 5.799 7.04e-09 ***
## x19 2.193e-04 1.310e-04 1.673 0.094330 .
## x20 -7.691e-04 9.129e-04 -0.842 0.399556
## x21 1.318e-04 3.344e-05 3.941 8.23e-05 ***
## x22 -7.568e-04 2.743e-04 -2.759 0.005810 **
## x23 1.472e-04 2.604e-04 0.565 0.571800
## stat1 -7.547e-05 1.970e-04 -0.383 0.701657
## stat2 -1.134e-04 1.955e-04 -0.580 0.561818
## stat3 5.369e-04 1.978e-04 2.714 0.006678 **
## stat4 -5.086e-04 1.988e-04 -2.558 0.010541 *
## stat5 -2.534e-04 1.978e-04 -1.281 0.200102
## stat6 -3.530e-04 1.963e-04 -1.798 0.072176 .
## stat7 -1.038e-04 1.969e-04 -0.527 0.598257
## stat8 -9.570e-05 1.970e-04 -0.486 0.627222
## stat9 7.079e-05 1.971e-04 0.359 0.719441
## stat10 -2.187e-04 1.964e-04 -1.114 0.265512
## stat11 -1.377e-04 1.984e-04 -0.694 0.487457
## stat12 1.589e-04 1.959e-04 0.811 0.417249
## stat13 -4.785e-04 1.959e-04 -2.442 0.014629 *
## stat14 -8.648e-04 1.954e-04 -4.425 9.83e-06 ***
## stat15 -4.150e-04 1.957e-04 -2.121 0.033978 *
## stat16 -1.770e-04 1.956e-04 -0.905 0.365627
## stat17 -1.384e-04 1.947e-04 -0.711 0.477015
## stat18 -2.375e-04 1.947e-04 -1.220 0.222438
## stat19 3.248e-04 1.978e-04 1.642 0.100597
## stat20 -1.129e-04 1.956e-04 -0.577 0.563779
## stat21 -5.488e-05 1.973e-04 -0.278 0.780914
## stat22 -3.026e-04 1.975e-04 -1.532 0.125476
## stat23 7.229e-04 1.962e-04 3.685 0.000231 ***
## stat24 -3.859e-04 1.966e-04 -1.963 0.049694 *
## stat25 -1.443e-04 1.957e-04 -0.737 0.461129
## stat26 -2.538e-04 1.973e-04 -1.286 0.198489
## stat27 2.993e-04 1.974e-04 1.517 0.129430
## stat28 -9.536e-06 1.971e-04 -0.048 0.961424
## stat29 -8.771e-05 1.988e-04 -0.441 0.659084
## stat30 1.057e-04 1.979e-04 0.534 0.593221
## stat31 9.630e-05 1.981e-04 0.486 0.626952
## stat32 5.959e-05 1.977e-04 0.301 0.763091
## stat33 -3.489e-04 1.966e-04 -1.774 0.076116 .
## stat34 2.825e-04 1.964e-04 1.438 0.150480
## stat35 -6.006e-04 1.970e-04 -3.048 0.002312 **
## stat36 -4.622e-05 1.957e-04 -0.236 0.813280
## stat37 -9.428e-05 1.993e-04 -0.473 0.636113
## stat38 4.888e-04 1.971e-04 2.480 0.013166 *
## stat39 -4.609e-04 1.955e-04 -2.358 0.018410 *
## stat40 -4.327e-05 1.970e-04 -0.220 0.826174
## stat41 -2.629e-04 1.959e-04 -1.342 0.179577
## stat42 -3.969e-04 1.966e-04 -2.019 0.043549 *
## stat43 -3.109e-04 1.987e-04 -1.564 0.117780
## stat44 1.290e-04 1.975e-04 0.653 0.513700
## stat45 -2.767e-04 1.963e-04 -1.409 0.158808
## stat46 2.261e-04 1.970e-04 1.147 0.251231
## stat47 1.141e-04 1.985e-04 0.575 0.565479
## stat48 1.641e-04 1.962e-04 0.837 0.402758
## stat49 5.132e-05 1.957e-04 0.262 0.793112
## stat50 2.903e-04 1.960e-04 1.481 0.138686
## stat51 3.170e-04 1.961e-04 1.617 0.105989
## stat52 -3.319e-05 1.975e-04 -0.168 0.866589
## stat53 -3.031e-05 1.986e-04 -0.153 0.878696
## stat54 -5.294e-04 1.984e-04 -2.669 0.007632 **
## stat55 2.381e-04 1.947e-04 1.223 0.221440
## stat56 -3.605e-05 1.958e-04 -0.184 0.853926
## stat57 1.108e-05 1.961e-04 0.056 0.954961
## stat58 -2.420e-04 1.948e-04 -1.242 0.214127
## stat59 2.476e-04 1.959e-04 1.264 0.206390
## stat60 6.687e-04 1.979e-04 3.379 0.000731 ***
## stat61 -8.243e-05 1.962e-04 -0.420 0.674345
## stat62 -4.589e-04 1.969e-04 -2.330 0.019827 *
## stat63 1.908e-04 1.968e-04 0.969 0.332555
## stat64 -2.814e-06 1.962e-04 -0.014 0.988555
## stat65 -1.279e-04 1.973e-04 -0.648 0.516904
## stat66 1.005e-04 1.984e-04 0.506 0.612546
## stat67 1.622e-04 1.972e-04 0.823 0.410792
## stat68 -1.712e-04 1.972e-04 -0.868 0.385498
## stat69 -8.333e-05 1.966e-04 -0.424 0.671700
## stat70 1.931e-04 1.959e-04 0.985 0.324451
## stat71 1.889e-04 1.959e-04 0.964 0.335139
## stat72 2.400e-04 1.990e-04 1.206 0.227813
## stat73 -1.953e-05 1.975e-04 -0.099 0.921221
## stat74 4.592e-05 1.975e-04 0.233 0.816109
## stat75 2.023e-04 1.979e-04 1.022 0.306603
## stat76 1.106e-05 1.971e-04 0.056 0.955282
## stat77 1.716e-04 1.978e-04 0.868 0.385525
## stat78 -4.004e-04 1.972e-04 -2.030 0.042355 *
## stat79 1.110e-04 1.963e-04 0.565 0.571957
## stat80 3.113e-04 1.971e-04 1.579 0.114293
## stat81 1.799e-04 1.969e-04 0.914 0.360866
## stat82 1.298e-04 1.969e-04 0.659 0.509662
## stat83 8.100e-05 1.963e-04 0.413 0.679924
## stat84 -9.116e-05 1.958e-04 -0.466 0.641566
## stat85 -2.599e-04 1.970e-04 -1.319 0.187091
## stat86 4.105e-04 1.972e-04 2.081 0.037439 *
## stat87 -1.930e-04 1.975e-04 -0.977 0.328452
## stat88 -2.091e-05 1.953e-04 -0.107 0.914712
## stat89 -1.144e-04 1.969e-04 -0.581 0.561381
## stat90 -3.778e-04 1.969e-04 -1.919 0.055011 .
## stat91 -4.045e-04 1.945e-04 -2.080 0.037587 *
## stat92 -2.420e-04 1.965e-04 -1.231 0.218192
## stat93 1.385e-04 1.993e-04 0.695 0.487245
## stat94 1.027e-04 1.964e-04 0.523 0.600921
## stat95 2.770e-04 1.961e-04 1.413 0.157831
## stat96 -1.981e-04 1.967e-04 -1.007 0.313905
## stat97 2.285e-04 1.951e-04 1.171 0.241539
## stat98 3.324e-03 1.943e-04 17.105 < 2e-16 ***
## stat99 3.533e-04 1.983e-04 1.782 0.074789 .
## stat100 4.570e-04 1.972e-04 2.317 0.020521 *
## stat101 -9.718e-05 1.977e-04 -0.492 0.623002
## stat102 4.805e-05 1.963e-04 0.245 0.806660
## stat103 -1.484e-04 1.999e-04 -0.742 0.457819
## stat104 -3.565e-04 1.954e-04 -1.825 0.068108 .
## stat105 3.237e-04 1.952e-04 1.658 0.097366 .
## stat106 -2.432e-04 1.955e-04 -1.244 0.213512
## stat107 -1.904e-04 1.963e-04 -0.970 0.332216
## stat108 -1.317e-04 1.971e-04 -0.669 0.503821
## stat109 6.734e-05 1.970e-04 0.342 0.732498
## stat110 -3.308e-03 1.949e-04 -16.977 < 2e-16 ***
## stat111 2.198e-05 1.966e-04 0.112 0.910989
## stat112 4.976e-05 1.982e-04 0.251 0.801751
## stat113 -1.682e-04 1.983e-04 -0.848 0.396323
## stat114 5.322e-04 1.969e-04 2.703 0.006894 **
## stat115 2.002e-04 1.957e-04 1.023 0.306454
## stat116 2.329e-04 1.973e-04 1.180 0.237895
## stat117 1.828e-04 1.967e-04 0.929 0.352749
## stat118 6.526e-05 1.955e-04 0.334 0.738515
## stat119 3.011e-04 1.975e-04 1.525 0.127307
## stat120 -1.319e-04 1.946e-04 -0.678 0.497827
## stat121 -1.145e-04 1.972e-04 -0.581 0.561493
## stat122 -9.039e-07 1.966e-04 -0.005 0.996332
## stat123 2.336e-04 1.991e-04 1.173 0.240778
## stat124 -2.514e-04 1.963e-04 -1.281 0.200337
## stat125 3.119e-04 1.980e-04 1.576 0.115189
## stat126 4.047e-04 1.961e-04 2.064 0.039054 *
## stat127 -3.627e-05 1.966e-04 -0.185 0.853628
## stat128 -5.350e-04 1.963e-04 -2.725 0.006453 **
## stat129 -1.973e-04 1.954e-04 -1.010 0.312695
## stat130 1.279e-04 1.973e-04 0.648 0.516894
## stat131 1.782e-05 1.971e-04 0.090 0.927945
## stat132 -1.443e-04 1.958e-04 -0.737 0.461014
## stat133 1.338e-04 1.970e-04 0.679 0.496945
## stat134 9.696e-05 1.953e-04 0.497 0.619546
## stat135 -7.397e-05 1.980e-04 -0.374 0.708696
## stat136 -8.079e-05 1.972e-04 -0.410 0.682056
## stat137 3.162e-04 1.956e-04 1.616 0.106104
## stat138 4.298e-05 1.966e-04 0.219 0.826913
## stat139 2.152e-04 1.976e-04 1.089 0.276196
## stat140 1.620e-04 1.954e-04 0.829 0.407178
## stat141 4.780e-04 1.945e-04 2.457 0.014038 *
## stat142 -1.149e-05 1.988e-04 -0.058 0.953902
## stat143 2.501e-04 1.973e-04 1.267 0.205042
## stat144 5.343e-04 1.956e-04 2.731 0.006340 **
## stat145 3.496e-05 1.992e-04 0.176 0.860684
## stat146 -5.035e-04 1.986e-04 -2.535 0.011282 *
## stat147 -4.711e-04 1.981e-04 -2.379 0.017405 *
## stat148 -3.531e-04 1.944e-04 -1.817 0.069326 .
## stat149 -5.253e-04 1.987e-04 -2.644 0.008213 **
## stat150 -8.915e-05 1.976e-04 -0.451 0.651806
## stat151 6.291e-06 1.992e-04 0.032 0.974806
## stat152 9.890e-07 1.952e-04 0.005 0.995958
## stat153 1.387e-04 1.987e-04 0.698 0.485192
## stat154 1.716e-04 1.990e-04 0.863 0.388413
## stat155 2.613e-04 1.970e-04 1.326 0.184800
## stat156 4.299e-04 1.972e-04 2.180 0.029278 *
## stat157 -8.384e-05 1.956e-04 -0.429 0.668245
## stat158 3.115e-04 1.994e-04 1.562 0.118340
## stat159 -2.136e-04 1.962e-04 -1.089 0.276402
## stat160 2.673e-04 1.979e-04 1.351 0.176857
## stat161 8.528e-05 1.979e-04 0.431 0.666521
## stat162 -5.591e-05 1.941e-04 -0.288 0.773337
## stat163 7.678e-05 1.990e-04 0.386 0.699673
## stat164 -6.012e-06 1.980e-04 -0.030 0.975784
## stat165 3.829e-05 1.960e-04 0.195 0.845130
## stat166 -1.489e-04 1.946e-04 -0.765 0.444305
## stat167 -2.455e-04 1.967e-04 -1.248 0.212052
## stat168 2.205e-06 1.960e-04 0.011 0.991023
## stat169 1.986e-04 1.978e-04 1.004 0.315463
## stat170 -2.179e-04 1.961e-04 -1.111 0.266428
## stat171 1.952e-05 1.977e-04 0.099 0.921327
## stat172 4.937e-04 1.955e-04 2.525 0.011604 *
## stat173 -1.631e-04 1.979e-04 -0.824 0.410010
## stat174 -2.071e-05 1.968e-04 -0.105 0.916190
## stat175 -2.949e-04 1.974e-04 -1.494 0.135204
## stat176 -2.171e-04 1.965e-04 -1.105 0.269104
## stat177 -3.331e-04 1.971e-04 -1.690 0.091123 .
## stat178 2.132e-04 1.987e-04 1.073 0.283289
## stat179 1.528e-04 1.962e-04 0.779 0.436144
## stat180 2.659e-04 1.960e-04 1.356 0.175000
## stat181 1.914e-04 1.980e-04 0.967 0.333565
## stat182 2.934e-04 1.973e-04 1.487 0.137059
## stat183 2.828e-04 1.964e-04 1.440 0.149894
## stat184 2.131e-04 1.977e-04 1.078 0.281243
## stat185 1.447e-05 1.961e-04 0.074 0.941174
## stat186 2.248e-04 1.976e-04 1.138 0.255272
## stat187 -4.821e-04 1.962e-04 -2.457 0.014053 *
## stat188 3.014e-04 1.958e-04 1.540 0.123682
## stat189 -2.138e-04 1.977e-04 -1.081 0.279658
## stat190 -8.102e-06 1.957e-04 -0.041 0.966977
## stat191 -3.228e-04 1.962e-04 -1.645 0.100022
## stat192 -1.023e-05 1.995e-04 -0.051 0.959117
## stat193 1.409e-04 1.992e-04 0.707 0.479336
## stat194 -7.896e-05 1.968e-04 -0.401 0.688300
## stat195 -8.602e-06 1.976e-04 -0.044 0.965276
## stat196 6.474e-06 1.995e-04 0.032 0.974109
## stat197 -1.091e-05 1.958e-04 -0.056 0.955564
## stat198 -3.880e-04 1.965e-04 -1.974 0.048427 *
## stat199 3.674e-04 1.952e-04 1.882 0.059918 .
## stat200 -1.573e-04 1.951e-04 -0.806 0.420038
## stat201 3.990e-05 1.962e-04 0.203 0.838861
## stat202 -3.200e-05 1.992e-04 -0.161 0.872376
## stat203 1.888e-04 1.954e-04 0.966 0.333856
## stat204 -3.321e-04 1.954e-04 -1.700 0.089210 .
## stat205 3.263e-05 1.951e-04 0.167 0.867225
## stat206 -1.969e-05 1.977e-04 -0.100 0.920686
## stat207 1.945e-04 1.965e-04 0.990 0.322272
## stat208 2.993e-04 1.982e-04 1.510 0.130982
## stat209 -2.318e-04 1.966e-04 -1.179 0.238438
## stat210 -2.467e-04 1.969e-04 -1.253 0.210286
## stat211 -7.122e-05 1.963e-04 -0.363 0.716755
## stat212 5.328e-07 1.980e-04 0.003 0.997853
## stat213 -1.250e-04 1.980e-04 -0.631 0.527982
## stat214 -3.081e-04 1.979e-04 -1.556 0.119657
## stat215 -2.796e-04 1.970e-04 -1.420 0.155787
## stat216 -2.625e-04 1.965e-04 -1.336 0.181617
## stat217 2.287e-04 1.971e-04 1.160 0.245993
## sqrt.x18 2.671e-02 7.486e-04 35.684 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02521 on 5467 degrees of freedom
## Multiple R-squared: 0.3835, Adjusted R-squared: 0.3565
## F-statistic: 14.17 on 240 and 5467 DF, p-value: < 2.2e-16cd.full2 = plot.diagnostics(model.full2, data.train2)
## [1] "Number of data points that have Cook's D > 4/n: 290"
## [1] "Number of data points that have Cook's D > 1: 0"# much more normal residuals than before.
# Checking to see if distributions are different and if so whcih variables
# High Leverage Plot
plotData = data.train %>%
rownames_to_column() %>%
mutate(type=ifelse(rowname %in% high.cd,'High','Normal')) %>%
dplyr::select(type,target=one_of(label.names))
ggplot(data=plotData, aes(x=type,y=target)) +
geom_boxplot(fill='light blue',outlier.shape=NA) +
scale_y_continuous(name="Target Variable Values") +
theme_light() +
ggtitle('Distribution of High Leverage Points and Normal Points')
plotData = data.train %>%
rownames_to_column() %>%
mutate(type=ifelse(rowname %in% high.cd,'High','Normal')) %>%
dplyr::select(type,one_of(feature.names))# 2 sample t-tests
comp.test = lapply(dplyr::select(plotData, one_of(feature.names)), function(x) t.test(x ~ plotData$type, var.equal = TRUE))
sig.comp = list.filter(comp.test, p.value < 0.05)
sapply(sig.comp, function(x) x[['p.value']])## x4 stat4 stat38 stat74 stat98
## 2.951218e-03 2.255000e-02 2.096412e-02 4.118559e-03 2.599100e-07
## stat110 stat128 stat145 stat156 stat214
## 7.088790e-04 1.487188e-02 2.074713e-02 2.574454e-02 5.600066e-03
## sqrt.x18
## 1.053180e-02# Distribution (box) Plots
mm = melt(plotData, id=c('type'))
ggplot(mm) +
geom_boxplot(aes(x=type, y=value))+
facet_wrap(~variable, ncol=10, scales = 'free') +
ggtitle('Distribution of High Leverage Points and Normal Points')
ggsave('comparison.jpeg', width =50, height = 400, units='cm',limitsize = FALSE)Grand Means Model
model.null = lm(grand.mean.formula, data.train)model.null2 = lm(grand.mean.formula, data.train2)Variable Selection
Basic: http://www.stat.columbia.edu/~martin/W2024/R10.pdf Cross Validation + Other Metrics: http://www.sthda.com/english/articles/37-model-selection-essentials-in-r/154-stepwise-regression-essentials-in-r/
Forward Selection (w/ full train)
Train
if (algo.forward == TRUE){
t1 = Sys.time()
model.forward = step(model.null, scope=list(lower=model.null, upper=model.full), direction="forward", trace = 0)
print(summary(model.forward))
t2 = Sys.time()
print (paste("Time taken for Forward Selection: ",t2-t1, sep = ""))
plot.diagnostics(model.forward, data.train)
}Test
if (algo.forward == TRUE){
test.model(model.forward, data.test, "Forward Selection")
}Forward Selection (w/ filtered train)
Train
if (algo.forward == TRUE){
t1 = Sys.time()
model.forward2 = step(model.null2, scope=list(lower=model.null2, upper=model.full2), direction="forward", trace = 0)
print(summary(model.forward2))
t2 = Sys.time()
print (paste("Time taken for Forward Selection: ",t2-t1, sep = ""))
plot.diagnostics(model.forward2, data.train2)
}Test
if (algo.forward == TRUE){
test.model(model.forward2, data.test, "Forward Selection (2)")
}Forward Selection with CV (w/ full train)
Train
if (algo.forward.caret == TRUE){
set.seed(1)
returned = train.caret.glmselect(formula = formula
, data = data.train
, method = "leapForward"
, feature.names = feature.names)
model.forward = returned$model
id = returned$id
}## Aggregating results
## Selecting tuning parameters
## Fitting nvmax = 7 on full training set
## nvmax RMSE Rsquared MAE RMSESD RsquaredSD
## 1 1 0.03410610 0.1149630 0.02657577 0.0012861157 0.02115810
## 2 2 0.03328304 0.1574261 0.02582705 0.0011082962 0.02564652
## 3 3 0.03267992 0.1875344 0.02522129 0.0010042512 0.02555459
## 4 4 0.03218662 0.2115343 0.02451101 0.0010005851 0.02634272
## 5 5 0.03186632 0.2271994 0.02432100 0.0009679660 0.02729495
## 6 6 0.03185480 0.2277527 0.02432260 0.0009428484 0.02753486
## 7 7 0.03174204 0.2329557 0.02426652 0.0009365345 0.02626930
## 8 8 0.03177661 0.2313360 0.02430354 0.0009495410 0.02509086
## 9 9 0.03177342 0.2315882 0.02429654 0.0009401013 0.02649936
## 10 10 0.03174421 0.2330436 0.02428987 0.0009336330 0.02724717
## 11 11 0.03175240 0.2327719 0.02430959 0.0009426783 0.02703386
## 12 12 0.03174790 0.2330175 0.02429329 0.0009361445 0.02713821
## 13 13 0.03176566 0.2322273 0.02431153 0.0009301109 0.02725054
## 14 14 0.03176252 0.2324098 0.02431077 0.0009013124 0.02867587
## 15 15 0.03175213 0.2328987 0.02430099 0.0009248676 0.02826833
## 16 16 0.03177382 0.2318280 0.02431036 0.0009445884 0.02676660
## 17 17 0.03178911 0.2311487 0.02432604 0.0009658569 0.02635184
## 18 18 0.03177643 0.2317125 0.02431717 0.0009793686 0.02490692
## 19 19 0.03175964 0.2325262 0.02429739 0.0009456329 0.02627921
## 20 20 0.03175338 0.2328159 0.02429680 0.0009786020 0.02653806
## 21 21 0.03174686 0.2331350 0.02429265 0.0009894709 0.02546913
## 22 22 0.03176649 0.2322184 0.02430019 0.0010005594 0.02502278
## 23 23 0.03176655 0.2322583 0.02430402 0.0009852345 0.02545103
## 24 24 0.03178902 0.2312438 0.02432037 0.0009709799 0.02507217
## 25 25 0.03179492 0.2309855 0.02432014 0.0009811234 0.02505781
## 26 26 0.03178208 0.2316431 0.02431141 0.0009527559 0.02605748
## 27 27 0.03178311 0.2316538 0.02430944 0.0009280740 0.02642813
## 28 28 0.03180855 0.2305068 0.02432795 0.0009411352 0.02680483
## 29 29 0.03181247 0.2303779 0.02433534 0.0009577700 0.02708067
## 30 30 0.03181153 0.2305138 0.02433202 0.0009699271 0.02718517
## 31 31 0.03181772 0.2302175 0.02433785 0.0009741382 0.02736658
## 32 32 0.03184322 0.2290545 0.02435844 0.0009838733 0.02646426
## 33 33 0.03184302 0.2290742 0.02436060 0.0009968974 0.02635739
## 34 34 0.03184450 0.2290212 0.02436921 0.0009797616 0.02609372
## 35 35 0.03185779 0.2284280 0.02437936 0.0009851665 0.02444185
## 36 36 0.03187608 0.2276434 0.02439540 0.0009810612 0.02421871
## 37 37 0.03188824 0.2271375 0.02439858 0.0009854518 0.02338710
## 38 38 0.03189828 0.2266973 0.02440059 0.0009976156 0.02343964
## 39 39 0.03190292 0.2265045 0.02440543 0.0010080669 0.02323980
## 40 40 0.03189997 0.2267181 0.02440931 0.0010068500 0.02319182
## 41 41 0.03189848 0.2268175 0.02441118 0.0010091399 0.02303736
## 42 42 0.03191460 0.2261415 0.02443213 0.0010199448 0.02338456
## 43 43 0.03192977 0.2254941 0.02444085 0.0010118629 0.02346942
## 44 44 0.03193267 0.2254000 0.02444851 0.0010044884 0.02338520
## 45 45 0.03193616 0.2252877 0.02445102 0.0009973765 0.02400841
## 46 46 0.03193405 0.2254265 0.02445603 0.0009882143 0.02454566
## 47 47 0.03193728 0.2252663 0.02446391 0.0009950543 0.02428079
## 48 48 0.03194665 0.2249123 0.02446199 0.0009887522 0.02445599
## 49 49 0.03195804 0.2244312 0.02447236 0.0009910758 0.02471262
## 50 50 0.03195650 0.2245477 0.02447378 0.0009856750 0.02497266
## 51 51 0.03196159 0.2243297 0.02448205 0.0009940737 0.02482403
## 52 52 0.03196289 0.2242614 0.02447613 0.0010036587 0.02504229
## 53 53 0.03195757 0.2245033 0.02447106 0.0010117905 0.02475876
## 54 54 0.03195437 0.2246278 0.02446148 0.0010232917 0.02432849
## 55 55 0.03195577 0.2245963 0.02446257 0.0010207142 0.02420678
## 56 56 0.03196041 0.2244640 0.02446725 0.0010321496 0.02469363
## 57 57 0.03196378 0.2243619 0.02446573 0.0010160059 0.02507184
## 58 58 0.03196930 0.2241456 0.02447356 0.0010082554 0.02515475
## 59 59 0.03197609 0.2238863 0.02448818 0.0010116584 0.02565957
## 60 60 0.03197623 0.2239077 0.02448726 0.0009923077 0.02562587
## 61 61 0.03197356 0.2240548 0.02448375 0.0009935484 0.02567438
## 62 62 0.03196472 0.2244544 0.02447808 0.0009947980 0.02602750
## 63 63 0.03197566 0.2240089 0.02449004 0.0009868922 0.02599832
## 64 64 0.03199182 0.2233471 0.02449905 0.0009751642 0.02612664
## 65 65 0.03198888 0.2234645 0.02449935 0.0009855925 0.02649073
## 66 66 0.03199523 0.2232116 0.02451188 0.0009812827 0.02689908
## 67 67 0.03198776 0.2235500 0.02449831 0.0009814838 0.02679456
## 68 68 0.03199896 0.2230752 0.02451332 0.0009912635 0.02680591
## 69 69 0.03200049 0.2230180 0.02451345 0.0009977180 0.02682632
## 70 70 0.03201164 0.2225768 0.02451442 0.0009940761 0.02726234
## 71 71 0.03202129 0.2222111 0.02451776 0.0009843885 0.02775224
## 72 72 0.03202563 0.2220653 0.02452262 0.0009850751 0.02792345
## 73 73 0.03204676 0.2211429 0.02453492 0.0009933874 0.02783449
## 74 74 0.03204929 0.2210783 0.02453199 0.0009922574 0.02818577
## 75 75 0.03205565 0.2208305 0.02453178 0.0010011223 0.02788161
## 76 76 0.03205334 0.2209760 0.02453181 0.0010034120 0.02755142
## 77 77 0.03205503 0.2208989 0.02452906 0.0009933016 0.02757856
## 78 78 0.03205164 0.2210512 0.02453032 0.0009870924 0.02728188
## 79 79 0.03205331 0.2209749 0.02453541 0.0009953131 0.02738509
## 80 80 0.03207144 0.2201838 0.02454733 0.0009927354 0.02728707
## 81 81 0.03207381 0.2200840 0.02455528 0.0009827646 0.02757541
## 82 82 0.03207756 0.2199669 0.02454738 0.0009881664 0.02814396
## 83 83 0.03208927 0.2194606 0.02455404 0.0009868763 0.02803082
## 84 84 0.03208613 0.2195480 0.02454829 0.0009916769 0.02775853
## 85 85 0.03209195 0.2192816 0.02456166 0.0009922083 0.02785167
## 86 86 0.03208971 0.2193879 0.02455435 0.0009955592 0.02755396
## 87 87 0.03209958 0.2190192 0.02456120 0.0009908167 0.02800906
## 88 88 0.03210275 0.2189107 0.02456522 0.0009814057 0.02797314
## 89 89 0.03210411 0.2188411 0.02456811 0.0009794581 0.02782565
## 90 90 0.03210744 0.2187327 0.02456923 0.0009830991 0.02792252
## 91 91 0.03210329 0.2189450 0.02456455 0.0009804345 0.02767122
## 92 92 0.03210971 0.2186926 0.02456794 0.0009836154 0.02768268
## 93 93 0.03211038 0.2187165 0.02457156 0.0009831213 0.02820087
## 94 94 0.03211049 0.2187488 0.02457162 0.0009706660 0.02800638
## 95 95 0.03210658 0.2189634 0.02457076 0.0009687649 0.02829526
## 96 96 0.03210145 0.2191791 0.02457009 0.0009604467 0.02814503
## 97 97 0.03210086 0.2191903 0.02457377 0.0009629273 0.02782940
## 98 98 0.03210986 0.2188088 0.02457730 0.0009700764 0.02759556
## 99 99 0.03210583 0.2189933 0.02457608 0.0009747238 0.02728754
## 100 100 0.03211047 0.2188252 0.02457988 0.0009674759 0.02757551
## 101 101 0.03211634 0.2186142 0.02458643 0.0009652755 0.02783298
## 102 102 0.03211125 0.2188466 0.02457981 0.0009633215 0.02785471
## 103 103 0.03211827 0.2185675 0.02458321 0.0009681009 0.02790878
## 104 104 0.03211846 0.2185911 0.02458794 0.0009667362 0.02792534
## 105 105 0.03211896 0.2186209 0.02458961 0.0009756484 0.02798272
## 106 106 0.03212722 0.2182607 0.02459851 0.0009786752 0.02806739
## 107 107 0.03212924 0.2181784 0.02459697 0.0009806778 0.02806748
## 108 108 0.03212780 0.2182626 0.02459662 0.0009796664 0.02796011
## 109 109 0.03213865 0.2178166 0.02460437 0.0009862512 0.02758793
## 110 110 0.03214478 0.2175733 0.02460745 0.0009857648 0.02773176
## 111 111 0.03214810 0.2174479 0.02461015 0.0009807094 0.02784171
## 112 112 0.03214591 0.2175556 0.02460944 0.0009806207 0.02758687
## 113 113 0.03214832 0.2174673 0.02461167 0.0009840247 0.02769451
## 114 114 0.03214527 0.2176010 0.02461114 0.0009834985 0.02787513
## 115 115 0.03215373 0.2172432 0.02462180 0.0009776194 0.02796084
## 116 116 0.03215087 0.2173568 0.02461325 0.0009827046 0.02829031
## 117 117 0.03215802 0.2170614 0.02462091 0.0009819193 0.02775306
## 118 118 0.03216312 0.2168421 0.02462799 0.0009829035 0.02795487
## 119 119 0.03216474 0.2167672 0.02462885 0.0009873727 0.02809556
## 120 120 0.03216157 0.2168957 0.02462492 0.0009974608 0.02805021
## 121 121 0.03216470 0.2167609 0.02462647 0.0010010258 0.02804458
## 122 122 0.03216597 0.2167287 0.02462556 0.0010033535 0.02808583
## 123 123 0.03216481 0.2168006 0.02462378 0.0010009704 0.02810225
## 124 124 0.03216926 0.2166383 0.02462855 0.0009996470 0.02792532
## 125 125 0.03216932 0.2166353 0.02463203 0.0009945964 0.02797592
## 126 126 0.03217393 0.2164577 0.02463437 0.0009993933 0.02806645
## 127 127 0.03216744 0.2167382 0.02462955 0.0010042442 0.02786594
## 128 128 0.03217332 0.2165084 0.02463534 0.0010014789 0.02764626
## 129 129 0.03217859 0.2163088 0.02464352 0.0009975475 0.02786191
## 130 130 0.03218123 0.2162331 0.02464338 0.0009939854 0.02773141
## 131 131 0.03218162 0.2162365 0.02464373 0.0009989299 0.02793846
## 132 132 0.03218107 0.2162539 0.02464583 0.0010065093 0.02794963
## 133 133 0.03218022 0.2162953 0.02464673 0.0010052839 0.02767312
## 134 134 0.03218281 0.2162168 0.02464695 0.0010068244 0.02777557
## 135 135 0.03218303 0.2162312 0.02464624 0.0010035570 0.02776437
## 136 136 0.03218372 0.2162181 0.02464546 0.0010109204 0.02760551
## 137 137 0.03218635 0.2161092 0.02464751 0.0010155383 0.02753125
## 138 138 0.03217989 0.2163877 0.02464154 0.0010124750 0.02735140
## 139 139 0.03218060 0.2163518 0.02463871 0.0010175708 0.02722741
## 140 140 0.03217900 0.2164062 0.02463787 0.0010208253 0.02694497
## 141 141 0.03218386 0.2162214 0.02464259 0.0010205205 0.02697901
## 142 142 0.03219014 0.2159505 0.02465035 0.0010217486 0.02695589
## 143 143 0.03219280 0.2158616 0.02464893 0.0010192818 0.02656880
## 144 144 0.03219968 0.2155687 0.02465617 0.0010155572 0.02670750
## 145 145 0.03220238 0.2154839 0.02465871 0.0010172425 0.02702679
## 146 146 0.03220674 0.2153003 0.02465636 0.0010157900 0.02690586
## 147 147 0.03220568 0.2153226 0.02465336 0.0010175663 0.02676567
## 148 148 0.03220617 0.2153211 0.02464909 0.0010206365 0.02671193
## 149 149 0.03220728 0.2152768 0.02464984 0.0010275861 0.02685075
## 150 150 0.03220983 0.2151541 0.02465201 0.0010273610 0.02668501
## 151 151 0.03221399 0.2149944 0.02465438 0.0010329767 0.02681295
## 152 152 0.03221655 0.2148799 0.02465447 0.0010320598 0.02673990
## 153 153 0.03222109 0.2146883 0.02465798 0.0010364166 0.02692746
## 154 154 0.03222346 0.2145837 0.02465633 0.0010327055 0.02684972
## 155 155 0.03222458 0.2145428 0.02465876 0.0010283430 0.02698826
## 156 156 0.03223055 0.2142681 0.02466294 0.0010267949 0.02682007
## 157 157 0.03223244 0.2141778 0.02466335 0.0010283627 0.02681809
## 158 158 0.03223493 0.2141068 0.02466715 0.0010250022 0.02689644
## 159 159 0.03222989 0.2143191 0.02466452 0.0010339208 0.02697194
## 160 160 0.03223194 0.2142259 0.02466574 0.0010371214 0.02673095
## 161 161 0.03223099 0.2142843 0.02466311 0.0010366664 0.02700724
## 162 162 0.03222699 0.2144568 0.02465785 0.0010328488 0.02706012
## 163 163 0.03222562 0.2145108 0.02465966 0.0010363295 0.02693475
## 164 164 0.03222389 0.2145731 0.02465859 0.0010297172 0.02687916
## 165 165 0.03221732 0.2148746 0.02465428 0.0010289913 0.02702436
## 166 166 0.03221561 0.2149547 0.02465047 0.0010223334 0.02692227
## 167 167 0.03221863 0.2148427 0.02465484 0.0010242736 0.02695445
## 168 168 0.03221766 0.2148958 0.02465440 0.0010265455 0.02701275
## 169 169 0.03221879 0.2148565 0.02465630 0.0010298504 0.02717500
## 170 170 0.03222029 0.2147922 0.02465920 0.0010309931 0.02718452
## 171 171 0.03221949 0.2148474 0.02466131 0.0010319546 0.02712960
## 172 172 0.03222412 0.2146407 0.02466734 0.0010307912 0.02697888
## 173 173 0.03222168 0.2147597 0.02466679 0.0010283618 0.02697704
## 174 174 0.03222361 0.2146793 0.02466769 0.0010297597 0.02693682
## 175 175 0.03222688 0.2145505 0.02466627 0.0010305452 0.02698883
## 176 176 0.03223101 0.2143710 0.02467102 0.0010332428 0.02686259
## 177 177 0.03223419 0.2142410 0.02467619 0.0010330784 0.02684578
## 178 178 0.03223196 0.2143250 0.02467534 0.0010308731 0.02672281
## 179 179 0.03223089 0.2143774 0.02467329 0.0010305792 0.02676973
## 180 180 0.03223026 0.2143959 0.02467315 0.0010323724 0.02685293
## 181 181 0.03223327 0.2142669 0.02467612 0.0010346800 0.02693059
## 182 182 0.03223217 0.2143201 0.02467577 0.0010336938 0.02713226
## 183 183 0.03222900 0.2144554 0.02467361 0.0010341257 0.02708243
## 184 184 0.03223128 0.2143541 0.02467655 0.0010369733 0.02715954
## 185 185 0.03222617 0.2145783 0.02467325 0.0010385848 0.02728563
## 186 186 0.03222466 0.2146484 0.02467174 0.0010378095 0.02729797
## 187 187 0.03222344 0.2146895 0.02467223 0.0010372334 0.02733961
## 188 188 0.03222582 0.2145900 0.02467648 0.0010381219 0.02727544
## 189 189 0.03222725 0.2145114 0.02467832 0.0010422847 0.02720718
## 190 190 0.03222934 0.2144297 0.02468033 0.0010440887 0.02729145
## 191 191 0.03222885 0.2144459 0.02467938 0.0010426642 0.02733943
## 192 192 0.03222796 0.2144754 0.02467883 0.0010427452 0.02739401
## 193 193 0.03222977 0.2143947 0.02467996 0.0010433118 0.02740576
## 194 194 0.03222980 0.2144016 0.02467980 0.0010424844 0.02748528
## 195 195 0.03222863 0.2144611 0.02467906 0.0010437310 0.02755765
## 196 196 0.03222813 0.2144795 0.02467944 0.0010426368 0.02761224
## 197 197 0.03222894 0.2144380 0.02468128 0.0010438590 0.02752779
## 198 198 0.03223124 0.2143440 0.02468343 0.0010437758 0.02756062
## 199 199 0.03222885 0.2144447 0.02468117 0.0010448083 0.02760357
## 200 200 0.03223026 0.2143780 0.02468289 0.0010481938 0.02758377
## 201 201 0.03222937 0.2144093 0.02468108 0.0010476185 0.02764257
## 202 202 0.03223170 0.2143143 0.02468308 0.0010448119 0.02759767
## 203 203 0.03222986 0.2143986 0.02468219 0.0010429669 0.02758904
## 204 204 0.03223128 0.2143422 0.02468494 0.0010437813 0.02748991
## 205 205 0.03223160 0.2143291 0.02468513 0.0010444590 0.02757676
## 206 206 0.03223155 0.2143227 0.02468628 0.0010464766 0.02756180
## 207 207 0.03223266 0.2142757 0.02468721 0.0010461687 0.02757659
## 208 208 0.03223255 0.2142844 0.02468626 0.0010455797 0.02765948
## 209 209 0.03223383 0.2142319 0.02468739 0.0010453155 0.02759553
## 210 210 0.03223324 0.2142530 0.02468755 0.0010457994 0.02754280
## 211 211 0.03223372 0.2142303 0.02468854 0.0010472969 0.02749382
## 212 212 0.03223316 0.2142630 0.02468811 0.0010460692 0.02753992
## 213 213 0.03223363 0.2142432 0.02468894 0.0010461425 0.02750597
## 214 214 0.03223389 0.2142325 0.02468867 0.0010437209 0.02746146
## 215 215 0.03223403 0.2142238 0.02468962 0.0010456569 0.02733998
## 216 216 0.03223412 0.2142200 0.02469021 0.0010437127 0.02725333
## 217 217 0.03223395 0.2142295 0.02468961 0.0010437833 0.02725264
## 218 218 0.03223496 0.2141836 0.02469094 0.0010449065 0.02728730
## 219 219 0.03223496 0.2141848 0.02469090 0.0010438134 0.02724923
## 220 220 0.03223448 0.2142101 0.02469057 0.0010435869 0.02722076
## 221 221 0.03223504 0.2141853 0.02469117 0.0010445416 0.02720648
## 222 222 0.03223526 0.2141694 0.02469290 0.0010456283 0.02720763
## 223 223 0.03223507 0.2141796 0.02469298 0.0010452188 0.02726817
## 224 224 0.03223452 0.2142026 0.02469286 0.0010448990 0.02725018
## 225 225 0.03223502 0.2141819 0.02469298 0.0010445208 0.02725529
## 226 226 0.03223463 0.2141981 0.02469295 0.0010447328 0.02723686
## 227 227 0.03223413 0.2142199 0.02469274 0.0010449003 0.02723661
## 228 228 0.03223320 0.2142582 0.02469156 0.0010457992 0.02721714
## 229 229 0.03223319 0.2142607 0.02469212 0.0010442812 0.02721419
## 230 230 0.03223344 0.2142479 0.02469235 0.0010444791 0.02720711
## 231 231 0.03223373 0.2142371 0.02469240 0.0010450460 0.02721503
## 232 232 0.03223404 0.2142241 0.02469243 0.0010453876 0.02723733
## 233 233 0.03223429 0.2142132 0.02469274 0.0010456495 0.02726316
## 234 234 0.03223392 0.2142269 0.02469274 0.0010457886 0.02725864
## 235 235 0.03223414 0.2142182 0.02469299 0.0010459490 0.02725502
## 236 236 0.03223406 0.2142203 0.02469294 0.0010461860 0.02728085
## 237 237 0.03223403 0.2142210 0.02469284 0.0010459806 0.02725711
## 238 238 0.03223436 0.2142079 0.02469323 0.0010461352 0.02727336
## 239 239 0.03223419 0.2142160 0.02469319 0.0010460914 0.02727348
## 240 240 0.03223413 0.2142186 0.02469321 0.0010461107 0.02727501
## MAESD
## 1 0.0006039987
## 2 0.0005560515
## 3 0.0005161705
## 4 0.0005030416
## 5 0.0004516171
## 6 0.0004274403
## 7 0.0004386333
## 8 0.0004116784
## 9 0.0004027648
## 10 0.0003804190
## 11 0.0003827175
## 12 0.0003714371
## 13 0.0003718175
## 14 0.0003523685
## 15 0.0003596036
## 16 0.0003688301
## 17 0.0003957368
## 18 0.0004141335
## 19 0.0003935137
## 20 0.0004296221
## 21 0.0004323057
## 22 0.0004296525
## 23 0.0004172385
## 24 0.0004122492
## 25 0.0004154358
## 26 0.0004009508
## 27 0.0003980728
## 28 0.0004058109
## 29 0.0004167466
## 30 0.0004250386
## 31 0.0004337700
## 32 0.0004301885
## 33 0.0004471771
## 34 0.0004379250
## 35 0.0004411262
## 36 0.0004363331
## 37 0.0004392575
## 38 0.0004565744
## 39 0.0004661210
## 40 0.0004693537
## 41 0.0004759417
## 42 0.0004995609
## 43 0.0004991563
## 44 0.0004921434
## 45 0.0004823461
## 46 0.0004753556
## 47 0.0004692500
## 48 0.0004614746
## 49 0.0004599378
## 50 0.0004511619
## 51 0.0004427788
## 52 0.0004630591
## 53 0.0004476089
## 54 0.0004607557
## 55 0.0004626769
## 56 0.0004743938
## 57 0.0004682956
## 58 0.0004556125
## 59 0.0004582658
## 60 0.0004496977
## 61 0.0004355180
## 62 0.0004499623
## 63 0.0004377042
## 64 0.0004329507
## 65 0.0004487659
## 66 0.0004504556
## 67 0.0004448734
## 68 0.0004467243
## 69 0.0004533267
## 70 0.0004525668
## 71 0.0004500066
## 72 0.0004595261
## 73 0.0004659523
## 74 0.0004633336
## 75 0.0004738190
## 76 0.0004710438
## 77 0.0004553563
## 78 0.0004534410
## 79 0.0004623091
## 80 0.0004607528
## 81 0.0004629932
## 82 0.0004774089
## 83 0.0004692723
## 84 0.0004702074
## 85 0.0004685357
## 86 0.0004747136
## 87 0.0004754254
## 88 0.0004656919
## 89 0.0004571532
## 90 0.0004593809
## 91 0.0004576292
## 92 0.0004618782
## 93 0.0004665940
## 94 0.0004564907
## 95 0.0004527557
## 96 0.0004523501
## 97 0.0004464795
## 98 0.0004492267
## 99 0.0004541808
## 100 0.0004595979
## 101 0.0004568488
## 102 0.0004527737
## 103 0.0004615426
## 104 0.0004633774
## 105 0.0004661702
## 106 0.0004698064
## 107 0.0004620997
## 108 0.0004626297
## 109 0.0004563712
## 110 0.0004538615
## 111 0.0004472959
## 112 0.0004384031
## 113 0.0004418254
## 114 0.0004387025
## 115 0.0004387272
## 116 0.0004413632
## 117 0.0004367245
## 118 0.0004382542
## 119 0.0004441669
## 120 0.0004527998
## 121 0.0004540134
## 122 0.0004554744
## 123 0.0004482638
## 124 0.0004414662
## 125 0.0004403732
## 126 0.0004422014
## 127 0.0004372368
## 128 0.0004360230
## 129 0.0004394743
## 130 0.0004444777
## 131 0.0004498589
## 132 0.0004582883
## 133 0.0004528263
## 134 0.0004535245
## 135 0.0004529470
## 136 0.0004572064
## 137 0.0004618735
## 138 0.0004573353
## 139 0.0004604980
## 140 0.0004581574
## 141 0.0004556408
## 142 0.0004567445
## 143 0.0004501770
## 144 0.0004459109
## 145 0.0004480937
## 146 0.0004480417
## 147 0.0004480812
## 148 0.0004488093
## 149 0.0004564846
## 150 0.0004520880
## 151 0.0004547858
## 152 0.0004551548
## 153 0.0004606019
## 154 0.0004596377
## 155 0.0004564784
## 156 0.0004512367
## 157 0.0004489165
## 158 0.0004444062
## 159 0.0004537700
## 160 0.0004556477
## 161 0.0004569324
## 162 0.0004517880
## 163 0.0004559000
## 164 0.0004492941
## 165 0.0004476347
## 166 0.0004410134
## 167 0.0004458714
## 168 0.0004448503
## 169 0.0004505409
## 170 0.0004539409
## 171 0.0004526260
## 172 0.0004546233
## 173 0.0004514684
## 174 0.0004536435
## 175 0.0004531830
## 176 0.0004530818
## 177 0.0004552330
## 178 0.0004535006
## 179 0.0004542633
## 180 0.0004550379
## 181 0.0004552068
## 182 0.0004571177
## 183 0.0004592029
## 184 0.0004650437
## 185 0.0004668034
## 186 0.0004672555
## 187 0.0004682015
## 188 0.0004653387
## 189 0.0004674643
## 190 0.0004709530
## 191 0.0004718856
## 192 0.0004701853
## 193 0.0004715426
## 194 0.0004731784
## 195 0.0004751147
## 196 0.0004755968
## 197 0.0004751587
## 198 0.0004746165
## 199 0.0004743917
## 200 0.0004756202
## 201 0.0004749317
## 202 0.0004731103
## 203 0.0004717088
## 204 0.0004715263
## 205 0.0004730446
## 206 0.0004730972
## 207 0.0004723421
## 208 0.0004738401
## 209 0.0004727704
## 210 0.0004727383
## 211 0.0004726466
## 212 0.0004713520
## 213 0.0004704777
## 214 0.0004683633
## 215 0.0004682378
## 216 0.0004668158
## 217 0.0004658348
## 218 0.0004661913
## 219 0.0004653234
## 220 0.0004653834
## 221 0.0004657934
## 222 0.0004665774
## 223 0.0004669865
## 224 0.0004666995
## 225 0.0004664618
## 226 0.0004665780
## 227 0.0004670497
## 228 0.0004673052
## 229 0.0004657645
## 230 0.0004659874
## 231 0.0004664695
## 232 0.0004670043
## 233 0.0004673417
## 234 0.0004674003
## 235 0.0004671664
## 236 0.0004674086
## 237 0.0004670385
## 238 0.0004674181
## 239 0.0004674932
## 240 0.0004675061
## nvmax
## 7 7
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Coefficients of final model:
## (Intercept) x4 x7 x9 x17
## 2.006915e+00 -4.541413e-05 1.108905e-02 3.535570e-03 1.516178e-03
## stat98 stat110 sqrt.x18
## 3.579154e-03 -3.275780e-03 2.665541e-02Test
if (algo.forward.caret == TRUE){
test.model(model.forward, data.test
,method = 'leapForward',subopt = NULL
,formula = formula, feature.names = feature.names, label.names = label.names
,id = id
,draw.limits = TRUE, transformation = t)
}## [1] "Summary of predicted values: "
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.041 2.084 2.097 2.096 2.109 2.142
## [1] "leapForward Test MSE: 0.00103851731581386"
Forward Selection with CV (w/ filtered train)
Train
if (algo.forward.caret == TRUE){
set.seed(1)
returned = train.caret.glmselect(formula = formula
,data = data.train2
,method = "leapForward"
,feature.names = feature.names)
model.forward = returned$model
id = returned$id
}## Aggregating results
## Selecting tuning parameters
## Fitting nvmax = 13 on full training set
## nvmax RMSE Rsquared MAE RMSESD RsquaredSD
## 1 1 0.02888038 0.1564097 0.02335547 0.0006267618 0.02633141
## 2 2 0.02766787 0.2253658 0.02249776 0.0006867294 0.02103652
## 3 3 0.02710230 0.2568330 0.02192148 0.0008105304 0.02659149
## 4 4 0.02638810 0.2952697 0.02113655 0.0007311698 0.02534274
## 5 5 0.02604900 0.3130434 0.02090228 0.0006875295 0.02296316
## 6 6 0.02593917 0.3187854 0.02084831 0.0006494856 0.02073056
## 7 7 0.02589964 0.3209157 0.02084970 0.0006015737 0.02042737
## 8 8 0.02582820 0.3246426 0.02082087 0.0005550767 0.02247168
## 9 9 0.02579308 0.3265747 0.02082103 0.0005507879 0.02199516
## 10 10 0.02571588 0.3305729 0.02078122 0.0005582145 0.02228794
## 11 11 0.02568025 0.3325064 0.02074574 0.0005288941 0.02225932
## 12 12 0.02567812 0.3326191 0.02075803 0.0005158039 0.02239029
## 13 13 0.02564510 0.3343325 0.02073002 0.0005334347 0.02150174
## 14 14 0.02565553 0.3337725 0.02074496 0.0005376892 0.02133207
## 15 15 0.02566769 0.3331426 0.02075283 0.0005322738 0.02121877
## 16 16 0.02565106 0.3339861 0.02074031 0.0005400159 0.02232892
## 17 17 0.02565684 0.3337038 0.02075486 0.0005349747 0.02244334
## 18 18 0.02569034 0.3320320 0.02077927 0.0005175302 0.02160943
## 19 19 0.02569389 0.3318903 0.02078282 0.0005137277 0.02141399
## 20 20 0.02573786 0.3296690 0.02082724 0.0005226936 0.02185297
## 21 21 0.02573791 0.3297124 0.02083071 0.0005388669 0.02248823
## 22 22 0.02573041 0.3301518 0.02080928 0.0005332016 0.02255091
## 23 23 0.02574350 0.3295116 0.02081582 0.0004973838 0.02215473
## 24 24 0.02573690 0.3298500 0.02081135 0.0005095199 0.02236121
## 25 25 0.02573415 0.3300139 0.02080063 0.0005122607 0.02223397
## 26 26 0.02572575 0.3304568 0.02079660 0.0005218498 0.02297961
## 27 27 0.02571852 0.3308000 0.02078553 0.0005223274 0.02373763
## 28 28 0.02571385 0.3310634 0.02077578 0.0005157453 0.02280322
## 29 29 0.02571815 0.3308878 0.02077422 0.0005134859 0.02268174
## 30 30 0.02569992 0.3317881 0.02076354 0.0004990451 0.02220654
## 31 31 0.02568678 0.3324897 0.02075678 0.0005247647 0.02329734
## 32 32 0.02567728 0.3329797 0.02073529 0.0005156593 0.02228844
## 33 33 0.02568738 0.3324651 0.02074242 0.0005131801 0.02198079
## 34 34 0.02566987 0.3333285 0.02073632 0.0005119259 0.02166104
## 35 35 0.02568202 0.3327907 0.02074606 0.0005088787 0.02212313
## 36 36 0.02569147 0.3323278 0.02074950 0.0005057042 0.02282192
## 37 37 0.02568064 0.3328777 0.02074211 0.0005095204 0.02173139
## 38 38 0.02567586 0.3331335 0.02074376 0.0005165740 0.02174346
## 39 39 0.02568240 0.3327828 0.02074372 0.0005246375 0.02167826
## 40 40 0.02567495 0.3331743 0.02073701 0.0005299796 0.02227179
## 41 41 0.02568006 0.3329868 0.02073915 0.0005349256 0.02252003
## 42 42 0.02567453 0.3332758 0.02073374 0.0005294200 0.02308932
## 43 43 0.02566591 0.3336973 0.02073920 0.0005438494 0.02320727
## 44 44 0.02565431 0.3343120 0.02073307 0.0005461292 0.02299408
## 45 45 0.02567237 0.3334511 0.02074824 0.0005532594 0.02303382
## 46 46 0.02567381 0.3334262 0.02074910 0.0005682394 0.02375835
## 47 47 0.02568155 0.3330864 0.02075758 0.0005586737 0.02338164
## 48 48 0.02569824 0.3322812 0.02077519 0.0005546817 0.02418786
## 49 49 0.02570104 0.3321487 0.02077400 0.0005680431 0.02461376
## 50 50 0.02571142 0.3316351 0.02078885 0.0005804232 0.02489882
## 51 51 0.02571442 0.3314867 0.02079844 0.0005859051 0.02498729
## 52 52 0.02570643 0.3318926 0.02078968 0.0005651142 0.02448059
## 53 53 0.02570448 0.3319985 0.02079594 0.0005647993 0.02458664
## 54 54 0.02570008 0.3322451 0.02079083 0.0005623948 0.02488725
## 55 55 0.02570571 0.3320055 0.02078647 0.0005615709 0.02525683
## 56 56 0.02571157 0.3317780 0.02078676 0.0005623583 0.02572261
## 57 57 0.02571958 0.3314396 0.02079727 0.0005763937 0.02603417
## 58 58 0.02570665 0.3320842 0.02078197 0.0005840367 0.02614432
## 59 59 0.02571344 0.3317911 0.02078047 0.0005840275 0.02602833
## 60 60 0.02571848 0.3315404 0.02078966 0.0005814968 0.02572808
## 61 61 0.02573274 0.3308496 0.02080515 0.0005755032 0.02528611
## 62 62 0.02573031 0.3309954 0.02080132 0.0005885288 0.02580707
## 63 63 0.02573244 0.3309269 0.02080611 0.0005808763 0.02533706
## 64 64 0.02573647 0.3307713 0.02081634 0.0005789997 0.02519088
## 65 65 0.02574425 0.3304328 0.02082181 0.0005739618 0.02508796
## 66 66 0.02574452 0.3304191 0.02082541 0.0005848907 0.02508101
## 67 67 0.02575689 0.3298633 0.02083514 0.0006033778 0.02554772
## 68 68 0.02576289 0.3295965 0.02084398 0.0006039602 0.02518274
## 69 69 0.02577383 0.3291115 0.02085838 0.0006057775 0.02511462
## 70 70 0.02576371 0.3296298 0.02084878 0.0006158543 0.02526022
## 71 71 0.02576546 0.3295439 0.02084936 0.0006068938 0.02467139
## 72 72 0.02578122 0.3287644 0.02085646 0.0006157291 0.02503141
## 73 73 0.02579290 0.3282137 0.02086260 0.0006035951 0.02473353
## 74 74 0.02580101 0.3278441 0.02087103 0.0006123236 0.02470564
## 75 75 0.02580792 0.3274803 0.02087341 0.0006075667 0.02466853
## 76 76 0.02582609 0.3265720 0.02089007 0.0006111937 0.02455794
## 77 77 0.02583220 0.3262776 0.02089593 0.0006164874 0.02470749
## 78 78 0.02582887 0.3264494 0.02089038 0.0006141500 0.02486334
## 79 79 0.02583269 0.3262365 0.02089166 0.0006053101 0.02421727
## 80 80 0.02583528 0.3261101 0.02089238 0.0005960557 0.02373935
## 81 81 0.02584918 0.3254403 0.02090405 0.0005922022 0.02334219
## 82 82 0.02585329 0.3252721 0.02090699 0.0005926366 0.02343379
## 83 83 0.02585577 0.3252160 0.02090867 0.0005908288 0.02363335
## 84 84 0.02584808 0.3255843 0.02089844 0.0005827252 0.02336556
## 85 85 0.02585037 0.3254972 0.02089943 0.0005890078 0.02333031
## 86 86 0.02583862 0.3261045 0.02089142 0.0005945725 0.02382155
## 87 87 0.02584607 0.3257735 0.02089989 0.0005958183 0.02354021
## 88 88 0.02583926 0.3261322 0.02089671 0.0005942065 0.02348262
## 89 89 0.02583517 0.3263434 0.02089851 0.0005883128 0.02316163
## 90 90 0.02583292 0.3265166 0.02089475 0.0005822011 0.02311729
## 91 91 0.02583526 0.3264475 0.02090029 0.0005773574 0.02291823
## 92 92 0.02583102 0.3266078 0.02089689 0.0005790033 0.02278655
## 93 93 0.02582694 0.3267871 0.02089638 0.0005811920 0.02294305
## 94 94 0.02582753 0.3267742 0.02089902 0.0005810938 0.02271601
## 95 95 0.02582751 0.3267936 0.02090221 0.0005762862 0.02249956
## 96 96 0.02583250 0.3265290 0.02091264 0.0005819996 0.02284583
## 97 97 0.02582775 0.3267690 0.02090785 0.0005853393 0.02297981
## 98 98 0.02582920 0.3267016 0.02091045 0.0005719549 0.02301537
## 99 99 0.02582630 0.3268618 0.02090708 0.0005776203 0.02286541
## 100 100 0.02582663 0.3268332 0.02090802 0.0005715448 0.02266071
## 101 101 0.02582455 0.3269171 0.02089884 0.0005664844 0.02284631
## 102 102 0.02582768 0.3267794 0.02089932 0.0005735925 0.02309395
## 103 103 0.02582971 0.3267084 0.02089949 0.0005805433 0.02326023
## 104 104 0.02582058 0.3271586 0.02089195 0.0005733599 0.02308826
## 105 105 0.02581718 0.3273512 0.02088530 0.0005765757 0.02280133
## 106 106 0.02582280 0.3270696 0.02089589 0.0005817636 0.02305462
## 107 107 0.02581921 0.3272573 0.02089657 0.0005846524 0.02323672
## 108 108 0.02582004 0.3272680 0.02089951 0.0005913568 0.02373511
## 109 109 0.02582519 0.3270261 0.02091109 0.0005903359 0.02371377
## 110 110 0.02582233 0.3271437 0.02091073 0.0005920933 0.02357317
## 111 111 0.02581927 0.3272747 0.02090373 0.0005907401 0.02340704
## 112 112 0.02581525 0.3274459 0.02090770 0.0005939706 0.02301589
## 113 113 0.02580944 0.3277347 0.02090535 0.0005915893 0.02304532
## 114 114 0.02580411 0.3280406 0.02090793 0.0005925728 0.02316538
## 115 115 0.02580319 0.3280795 0.02090210 0.0005887298 0.02323966
## 116 116 0.02580665 0.3279118 0.02090360 0.0005895227 0.02326606
## 117 117 0.02580821 0.3278529 0.02090678 0.0005889767 0.02315982
## 118 118 0.02581345 0.3275854 0.02091063 0.0005901964 0.02311294
## 119 119 0.02581204 0.3276748 0.02090903 0.0005904491 0.02308205
## 120 120 0.02581219 0.3276579 0.02090637 0.0005926569 0.02312392
## 121 121 0.02581338 0.3276144 0.02090873 0.0005951203 0.02310454
## 122 122 0.02581695 0.3274764 0.02091197 0.0005987173 0.02323650
## 123 123 0.02581999 0.3273198 0.02090872 0.0005971357 0.02324535
## 124 124 0.02581774 0.3274335 0.02090824 0.0005972078 0.02291369
## 125 125 0.02582471 0.3270920 0.02090976 0.0005890575 0.02272593
## 126 126 0.02582092 0.3272666 0.02090829 0.0005934350 0.02305902
## 127 127 0.02582169 0.3272288 0.02090613 0.0005938942 0.02284735
## 128 128 0.02581330 0.3276200 0.02089985 0.0005998121 0.02324178
## 129 129 0.02582012 0.3272924 0.02090335 0.0006012107 0.02325979
## 130 130 0.02582201 0.3272052 0.02090716 0.0005946930 0.02299736
## 131 131 0.02582699 0.3269543 0.02090727 0.0005978385 0.02338814
## 132 132 0.02582214 0.3271944 0.02090352 0.0005927339 0.02326222
## 133 133 0.02582100 0.3272537 0.02090416 0.0005928219 0.02333666
## 134 134 0.02582074 0.3272742 0.02090326 0.0005925040 0.02323256
## 135 135 0.02581657 0.3274721 0.02089826 0.0005958984 0.02320979
## 136 136 0.02581141 0.3277214 0.02089217 0.0005951126 0.02321542
## 137 137 0.02580408 0.3280789 0.02088594 0.0006000015 0.02331117
## 138 138 0.02580074 0.3282591 0.02088132 0.0005991602 0.02318450
## 139 139 0.02580703 0.3279597 0.02088924 0.0005971830 0.02338305
## 140 140 0.02580980 0.3278374 0.02089143 0.0006039282 0.02371603
## 141 141 0.02580528 0.3280559 0.02088707 0.0006072903 0.02391055
## 142 142 0.02580955 0.3278478 0.02088850 0.0006054965 0.02370681
## 143 143 0.02581039 0.3278084 0.02089034 0.0006118165 0.02402034
## 144 144 0.02581359 0.3276753 0.02089307 0.0006128366 0.02391777
## 145 145 0.02581080 0.3278048 0.02088765 0.0006124281 0.02396928
## 146 146 0.02581515 0.3276284 0.02088952 0.0006084623 0.02376371
## 147 147 0.02581454 0.3276622 0.02089061 0.0006089706 0.02357194
## 148 148 0.02580781 0.3280042 0.02088859 0.0006057809 0.02362373
## 149 149 0.02580926 0.3279519 0.02088819 0.0006062277 0.02364296
## 150 150 0.02581014 0.3279253 0.02088789 0.0006077092 0.02368739
## 151 151 0.02581010 0.3279386 0.02088987 0.0006089543 0.02361237
## 152 152 0.02580769 0.3280627 0.02088935 0.0006076643 0.02348084
## 153 153 0.02580895 0.3279921 0.02089211 0.0006056340 0.02346460
## 154 154 0.02580520 0.3281775 0.02088972 0.0006046600 0.02339837
## 155 155 0.02580032 0.3284219 0.02088549 0.0006030231 0.02298145
## 156 156 0.02580518 0.3281819 0.02088718 0.0005988795 0.02276936
## 157 157 0.02580260 0.3283220 0.02088633 0.0005989289 0.02264011
## 158 158 0.02579259 0.3288038 0.02087615 0.0005995867 0.02255705
## 159 159 0.02579416 0.3287176 0.02087901 0.0006050863 0.02276787
## 160 160 0.02579251 0.3288107 0.02087626 0.0006075554 0.02296789
## 161 161 0.02578891 0.3289929 0.02087096 0.0006061642 0.02322577
## 162 162 0.02579177 0.3288602 0.02087244 0.0005980397 0.02284203
## 163 163 0.02578948 0.3289531 0.02087084 0.0006006023 0.02301775
## 164 164 0.02578468 0.3291950 0.02086274 0.0006018943 0.02321438
## 165 165 0.02578301 0.3292692 0.02085999 0.0005985934 0.02327075
## 166 166 0.02578189 0.3293226 0.02085765 0.0005986537 0.02335741
## 167 167 0.02578231 0.3292977 0.02085907 0.0006019399 0.02362461
## 168 168 0.02578555 0.3291534 0.02086337 0.0005999028 0.02346599
## 169 169 0.02578217 0.3293112 0.02086372 0.0005956168 0.02318824
## 170 170 0.02578096 0.3293592 0.02086478 0.0005924658 0.02302482
## 171 171 0.02578322 0.3292567 0.02086646 0.0005898892 0.02282490
## 172 172 0.02578498 0.3291808 0.02086932 0.0005885822 0.02257354
## 173 173 0.02578130 0.3293460 0.02086624 0.0005908088 0.02265845
## 174 174 0.02578177 0.3293328 0.02086480 0.0005911418 0.02277908
## 175 175 0.02578007 0.3294202 0.02086364 0.0005935627 0.02298045
## 176 176 0.02578012 0.3294104 0.02086498 0.0005895189 0.02289113
## 177 177 0.02578131 0.3293480 0.02086651 0.0005867510 0.02275149
## 178 178 0.02578453 0.3291940 0.02087077 0.0005834808 0.02281978
## 179 179 0.02578768 0.3290485 0.02087457 0.0005827820 0.02282550
## 180 180 0.02579176 0.3288471 0.02087795 0.0005814744 0.02273010
## 181 181 0.02579371 0.3287641 0.02087851 0.0005793947 0.02256623
## 182 182 0.02579362 0.3287812 0.02088016 0.0005822530 0.02261135
## 183 183 0.02579220 0.3288587 0.02087981 0.0005813521 0.02249112
## 184 184 0.02579391 0.3287742 0.02088190 0.0005817407 0.02254340
## 185 185 0.02579152 0.3288881 0.02087895 0.0005823267 0.02250522
## 186 186 0.02578965 0.3289754 0.02087811 0.0005834626 0.02248278
## 187 187 0.02579130 0.3289014 0.02087878 0.0005867068 0.02251575
## 188 188 0.02579301 0.3288260 0.02087891 0.0005862012 0.02250843
## 189 189 0.02579465 0.3287608 0.02088010 0.0005875391 0.02252813
## 190 190 0.02579247 0.3288567 0.02087889 0.0005868492 0.02248393
## 191 191 0.02579106 0.3289247 0.02087703 0.0005855321 0.02245265
## 192 192 0.02578817 0.3290553 0.02087490 0.0005806270 0.02227197
## 193 193 0.02578708 0.3291143 0.02087262 0.0005804337 0.02224749
## 194 194 0.02578869 0.3290309 0.02087353 0.0005810898 0.02233322
## 195 195 0.02579034 0.3289574 0.02087409 0.0005803654 0.02236416
## 196 196 0.02579178 0.3288788 0.02087456 0.0005815995 0.02239959
## 197 197 0.02579055 0.3289510 0.02087359 0.0005827314 0.02249009
## 198 198 0.02579214 0.3288843 0.02087546 0.0005835827 0.02248298
## 199 199 0.02579062 0.3289560 0.02087402 0.0005844707 0.02249908
## 200 200 0.02579174 0.3289030 0.02087644 0.0005845183 0.02248883
## 201 201 0.02579304 0.3288390 0.02087639 0.0005836284 0.02239165
## 202 202 0.02579276 0.3288520 0.02087631 0.0005837610 0.02233758
## 203 203 0.02579404 0.3287930 0.02087901 0.0005829146 0.02223242
## 204 204 0.02579558 0.3287190 0.02087922 0.0005834188 0.02228375
## 205 205 0.02579772 0.3286178 0.02088020 0.0005823138 0.02219107
## 206 206 0.02579892 0.3285654 0.02088128 0.0005809090 0.02223226
## 207 207 0.02579867 0.3285809 0.02088136 0.0005844181 0.02233892
## 208 208 0.02579890 0.3285743 0.02088098 0.0005839143 0.02231316
## 209 209 0.02579878 0.3285812 0.02088050 0.0005844861 0.02235314
## 210 210 0.02579904 0.3285756 0.02088019 0.0005868091 0.02241808
## 211 211 0.02579805 0.3286224 0.02087892 0.0005869460 0.02241573
## 212 212 0.02579992 0.3285328 0.02088027 0.0005853128 0.02238425
## 213 213 0.02580126 0.3284724 0.02088208 0.0005854671 0.02239519
## 214 214 0.02580049 0.3285105 0.02088185 0.0005856100 0.02240101
## 215 215 0.02580033 0.3285163 0.02088065 0.0005867739 0.02248883
## 216 216 0.02579989 0.3285411 0.02088081 0.0005862293 0.02247018
## 217 217 0.02580050 0.3285152 0.02088170 0.0005873485 0.02252026
## 218 218 0.02580119 0.3284856 0.02088152 0.0005879841 0.02253500
## 219 219 0.02580166 0.3284622 0.02088203 0.0005889135 0.02251416
## 220 220 0.02580203 0.3284464 0.02088195 0.0005886014 0.02248331
## 221 221 0.02580261 0.3284173 0.02088168 0.0005880471 0.02244028
## 222 222 0.02580418 0.3283414 0.02088338 0.0005884707 0.02243783
## 223 223 0.02580430 0.3283402 0.02088385 0.0005894817 0.02246021
## 224 224 0.02580460 0.3283266 0.02088451 0.0005893404 0.02246173
## 225 225 0.02580541 0.3282875 0.02088516 0.0005894300 0.02242379
## 226 226 0.02580578 0.3282686 0.02088568 0.0005898879 0.02243287
## 227 227 0.02580618 0.3282490 0.02088577 0.0005892798 0.02239710
## 228 228 0.02580530 0.3282953 0.02088497 0.0005896620 0.02240748
## 229 229 0.02580519 0.3282996 0.02088502 0.0005895789 0.02242132
## 230 230 0.02580500 0.3283086 0.02088452 0.0005890612 0.02242948
## 231 231 0.02580437 0.3283363 0.02088425 0.0005887867 0.02240553
## 232 232 0.02580456 0.3283292 0.02088410 0.0005887680 0.02239511
## 233 233 0.02580418 0.3283463 0.02088389 0.0005892820 0.02241764
## 234 234 0.02580336 0.3283860 0.02088309 0.0005897805 0.02244912
## 235 235 0.02580288 0.3284094 0.02088269 0.0005892806 0.02243559
## 236 236 0.02580313 0.3283991 0.02088306 0.0005894619 0.02244298
## 237 237 0.02580313 0.3283989 0.02088291 0.0005895992 0.02245239
## 238 238 0.02580317 0.3283959 0.02088292 0.0005895545 0.02245788
## 239 239 0.02580340 0.3283851 0.02088304 0.0005896238 0.02245255
## 240 240 0.02580348 0.3283813 0.02088310 0.0005896638 0.02245074
## MAESD
## 1 0.0004604171
## 2 0.0005295821
## 3 0.0005663676
## 4 0.0004831073
## 5 0.0004993662
## 6 0.0005070940
## 7 0.0004687843
## 8 0.0004502107
## 9 0.0004598547
## 10 0.0004621940
## 11 0.0004355906
## 12 0.0004338364
## 13 0.0004558980
## 14 0.0004441391
## 15 0.0004372342
## 16 0.0004432195
## 17 0.0004404419
## 18 0.0004403509
## 19 0.0004201433
## 20 0.0004166317
## 21 0.0004434090
## 22 0.0004507171
## 23 0.0004294308
## 24 0.0004461363
## 25 0.0004578300
## 26 0.0004739148
## 27 0.0004805325
## 28 0.0004711278
## 29 0.0004699987
## 30 0.0004448160
## 31 0.0004578997
## 32 0.0004497970
## 33 0.0004600843
## 34 0.0004645457
## 35 0.0004617882
## 36 0.0004560016
## 37 0.0004570345
## 38 0.0004657082
## 39 0.0004722526
## 40 0.0004838955
## 41 0.0004937431
## 42 0.0004851511
## 43 0.0005010650
## 44 0.0005048055
## 45 0.0005220898
## 46 0.0005348349
## 47 0.0005278490
## 48 0.0005193737
## 49 0.0005293235
## 50 0.0005574183
## 51 0.0005669865
## 52 0.0005470428
## 53 0.0005525746
## 54 0.0005405494
## 55 0.0005409315
## 56 0.0005491505
## 57 0.0005552629
## 58 0.0005624205
## 59 0.0005609408
## 60 0.0005593707
## 61 0.0005601565
## 62 0.0005656455
## 63 0.0005599612
## 64 0.0005586633
## 65 0.0005635173
## 66 0.0005755445
## 67 0.0005909006
## 68 0.0005861716
## 69 0.0005941534
## 70 0.0006051794
## 71 0.0005950312
## 72 0.0006112488
## 73 0.0006016017
## 74 0.0005990323
## 75 0.0005915225
## 76 0.0005951866
## 77 0.0006113831
## 78 0.0006065219
## 79 0.0005985225
## 80 0.0005903204
## 81 0.0005944194
## 82 0.0005981555
## 83 0.0005983609
## 84 0.0005913369
## 85 0.0005916722
## 86 0.0005836075
## 87 0.0005867378
## 88 0.0005856735
## 89 0.0005788857
## 90 0.0005723771
## 91 0.0005748425
## 92 0.0005725895
## 93 0.0005738091
## 94 0.0005674578
## 95 0.0005634736
## 96 0.0005723432
## 97 0.0005703462
## 98 0.0005583679
## 99 0.0005680595
## 100 0.0005648277
## 101 0.0005598380
## 102 0.0005716468
## 103 0.0005756319
## 104 0.0005663924
## 105 0.0005683870
## 106 0.0005781467
## 107 0.0005808023
## 108 0.0005848309
## 109 0.0005856044
## 110 0.0005938172
## 111 0.0005926137
## 112 0.0005918957
## 113 0.0005904336
## 114 0.0005895499
## 115 0.0005894774
## 116 0.0005883033
## 117 0.0005883101
## 118 0.0005840972
## 119 0.0005814825
## 120 0.0005810610
## 121 0.0005832566
## 122 0.0005857325
## 123 0.0005829023
## 124 0.0005890280
## 125 0.0005812478
## 126 0.0005848219
## 127 0.0005807152
## 128 0.0005824537
## 129 0.0005792415
## 130 0.0005756383
## 131 0.0005792144
## 132 0.0005797011
## 133 0.0005805971
## 134 0.0005870293
## 135 0.0005873961
## 136 0.0005915482
## 137 0.0005962929
## 138 0.0005897653
## 139 0.0005822294
## 140 0.0005854431
## 141 0.0005871587
## 142 0.0005875907
## 143 0.0005938466
## 144 0.0005966136
## 145 0.0005951390
## 146 0.0005935034
## 147 0.0005982994
## 148 0.0005957568
## 149 0.0005961702
## 150 0.0005937219
## 151 0.0005951351
## 152 0.0005945673
## 153 0.0005929232
## 154 0.0005909723
## 155 0.0005929431
## 156 0.0005893438
## 157 0.0005883026
## 158 0.0005899555
## 159 0.0005937603
## 160 0.0005917288
## 161 0.0005940671
## 162 0.0005861211
## 163 0.0005876271
## 164 0.0005873892
## 165 0.0005883226
## 166 0.0005886323
## 167 0.0005897915
## 168 0.0005871572
## 169 0.0005858670
## 170 0.0005822736
## 171 0.0005785971
## 172 0.0005809589
## 173 0.0005803280
## 174 0.0005813352
## 175 0.0005830233
## 176 0.0005801820
## 177 0.0005751898
## 178 0.0005727400
## 179 0.0005732457
## 180 0.0005758422
## 181 0.0005741822
## 182 0.0005754254
## 183 0.0005736250
## 184 0.0005736178
## 185 0.0005763587
## 186 0.0005790358
## 187 0.0005801744
## 188 0.0005793279
## 189 0.0005817473
## 190 0.0005808045
## 191 0.0005793380
## 192 0.0005764986
## 193 0.0005751428
## 194 0.0005751462
## 195 0.0005757867
## 196 0.0005770206
## 197 0.0005790611
## 198 0.0005795640
## 199 0.0005798079
## 200 0.0005814693
## 201 0.0005807335
## 202 0.0005808919
## 203 0.0005801940
## 204 0.0005819659
## 205 0.0005815627
## 206 0.0005808254
## 207 0.0005846257
## 208 0.0005848515
## 209 0.0005862627
## 210 0.0005877891
## 211 0.0005883912
## 212 0.0005875535
## 213 0.0005886005
## 214 0.0005868758
## 215 0.0005884643
## 216 0.0005874744
## 217 0.0005887474
## 218 0.0005902757
## 219 0.0005910193
## 220 0.0005901838
## 221 0.0005899732
## 222 0.0005903717
## 223 0.0005912456
## 224 0.0005909634
## 225 0.0005908327
## 226 0.0005912975
## 227 0.0005905947
## 228 0.0005909874
## 229 0.0005907499
## 230 0.0005903754
## 231 0.0005900148
## 232 0.0005902183
## 233 0.0005905081
## 234 0.0005910302
## 235 0.0005905733
## 236 0.0005909074
## 237 0.0005907941
## 238 0.0005905927
## 239 0.0005905728
## 240 0.0005905793
## nvmax
## 13 13
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Coefficients of final model:
## (Intercept) x4 x7 x8 x9
## 1.961247e+00 -5.186788e-05 1.234233e-02 5.954491e-04 3.245467e-03
## x10 x11 x16 x17 stat14
## 1.445759e-03 2.412145e+05 8.242512e-04 1.494800e-03 -8.523881e-04
## stat23 stat98 stat110 sqrt.x18
## 7.175290e-04 3.343452e-03 -3.289042e-03 2.672089e-02Test
if (algo.forward.caret == TRUE){
test.model(model.forward, data.test
,method = 'leapForward',subopt = NULL
,formula = formula, feature.names = feature.names, label.names = label.names
,id = id
,draw.limits = TRUE, transformation = t)
}## [1] "Summary of predicted values: "
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.035 2.081 2.093 2.093 2.106 2.145
## [1] "leapForward Test MSE: 0.00104891376260811"
Backward Elimination
Train
if (algo.backward == TRUE){
# Takes too much time
t1 = Sys.time()
model.backward = step(model.full, data = data.train, direction="backward", trace = 0)
print(summary(model.backward))
t2 = Sys.time()
print (paste("Time taken for Backward Elimination: ",t2-t1, sep = ""))
plot.diagnostics(model.backward, data.train)
}Test
if (algo.backward == TRUE){
test.model(model.backard, data.test, "Backward Elimination")
}Backward Elimination with CV (w/ full train)
Train
if (algo.backward.caret == TRUE){
set.seed(1)
returned = train.caret.glmselect(formula = formula
,data = data.train
,method = "leapBackward"
,feature.names = feature.names)
model.backward = returned$model
id = returned$id
}## Aggregating results
## Selecting tuning parameters
## Fitting nvmax = 21 on full training set
## nvmax RMSE Rsquared MAE RMSESD RsquaredSD
## 1 1 0.03410610 0.1149630 0.02657577 0.0012861157 0.02115810
## 2 2 0.03328304 0.1574261 0.02582705 0.0011082962 0.02564652
## 3 3 0.03267992 0.1875344 0.02522129 0.0010042512 0.02555459
## 4 4 0.03218662 0.2115343 0.02451101 0.0010005851 0.02634272
## 5 5 0.03186632 0.2271994 0.02432100 0.0009679660 0.02729495
## 6 6 0.03185480 0.2277527 0.02432260 0.0009428484 0.02753486
## 7 7 0.03174204 0.2329557 0.02426652 0.0009365345 0.02626930
## 8 8 0.03177661 0.2313360 0.02430354 0.0009495410 0.02509086
## 9 9 0.03177342 0.2315882 0.02429654 0.0009401013 0.02649936
## 10 10 0.03174421 0.2330436 0.02428987 0.0009336330 0.02724717
## 11 11 0.03175240 0.2327719 0.02430959 0.0009426783 0.02703386
## 12 12 0.03174790 0.2330175 0.02429329 0.0009361445 0.02713821
## 13 13 0.03175640 0.2326937 0.02430729 0.0009210531 0.02750259
## 14 14 0.03174821 0.2331087 0.02430265 0.0008876305 0.02909070
## 15 15 0.03175781 0.2326158 0.02430769 0.0009302689 0.02811104
## 16 16 0.03177382 0.2318280 0.02431036 0.0009445884 0.02676660
## 17 17 0.03178911 0.2311487 0.02432604 0.0009658569 0.02635184
## 18 18 0.03177643 0.2317125 0.02431717 0.0009793686 0.02490692
## 19 19 0.03175964 0.2325262 0.02429739 0.0009456329 0.02627921
## 20 20 0.03175338 0.2328159 0.02429680 0.0009786020 0.02653806
## 21 21 0.03173935 0.2334905 0.02428508 0.0009840306 0.02545556
## 22 22 0.03175723 0.2326583 0.02429844 0.0009827134 0.02523360
## 23 23 0.03177324 0.2319769 0.02431065 0.0009664176 0.02500459
## 24 24 0.03178101 0.2316240 0.02431494 0.0009746416 0.02498553
## 25 25 0.03178794 0.2313409 0.02430940 0.0009701570 0.02535667
## 26 26 0.03178550 0.2315099 0.02430897 0.0009525656 0.02613484
## 27 27 0.03178503 0.2316293 0.02430735 0.0009302627 0.02636797
## 28 28 0.03179885 0.2310416 0.02432687 0.0009317546 0.02652259
## 29 29 0.03180255 0.2309000 0.02432941 0.0009380593 0.02679688
## 30 30 0.03180062 0.2309386 0.02432064 0.0009642642 0.02624096
## 31 31 0.03180474 0.2307492 0.02432622 0.0009667548 0.02704769
## 32 32 0.03182643 0.2298072 0.02434847 0.0009654710 0.02666004
## 33 33 0.03183422 0.2294918 0.02435875 0.0009880193 0.02646569
## 34 34 0.03184403 0.2290906 0.02437048 0.0009826630 0.02640569
## 35 35 0.03186398 0.2281715 0.02437777 0.0009848969 0.02491418
## 36 36 0.03188623 0.2271867 0.02439776 0.0009772063 0.02510442
## 37 37 0.03189688 0.2267396 0.02440640 0.0009848116 0.02451533
## 38 38 0.03189749 0.2267378 0.02440712 0.0009965136 0.02346400
## 39 39 0.03189772 0.2267426 0.02440672 0.0009965048 0.02350484
## 40 40 0.03188308 0.2274345 0.02440556 0.0009967017 0.02308491
## 41 41 0.03189219 0.2270984 0.02440958 0.0010122723 0.02296109
## 42 42 0.03191386 0.2261857 0.02443666 0.0010135977 0.02329331
## 43 43 0.03193363 0.2253295 0.02444919 0.0010093464 0.02326704
## 44 44 0.03193395 0.2253455 0.02445311 0.0010057347 0.02336262
## 45 45 0.03193616 0.2252877 0.02445102 0.0009973765 0.02400841
## 46 46 0.03193004 0.2256258 0.02444756 0.0009919646 0.02463915
## 47 47 0.03193271 0.2255070 0.02445240 0.0009802941 0.02474200
## 48 48 0.03194622 0.2249318 0.02445795 0.0009862503 0.02449970
## 49 49 0.03196465 0.2241268 0.02447800 0.0009803599 0.02479855
## 50 50 0.03195945 0.2244088 0.02447681 0.0009803458 0.02500255
## 51 51 0.03197023 0.2239340 0.02448702 0.0009887533 0.02492097
## 52 52 0.03196299 0.2242518 0.02448094 0.0010084516 0.02482320
## 53 53 0.03195243 0.2247351 0.02447428 0.0010218759 0.02491747
## 54 54 0.03195447 0.2246253 0.02446889 0.0010292820 0.02443268
## 55 55 0.03194900 0.2248975 0.02446582 0.0010272742 0.02435764
## 56 56 0.03196646 0.2241897 0.02448574 0.0010297665 0.02479090
## 57 57 0.03196967 0.2240847 0.02448937 0.0010115033 0.02518276
## 58 58 0.03197233 0.2240167 0.02449291 0.0010150578 0.02526220
## 59 59 0.03197418 0.2239440 0.02449685 0.0010111162 0.02557746
## 60 60 0.03197957 0.2237494 0.02450787 0.0010029230 0.02554756
## 61 61 0.03198569 0.2234916 0.02450371 0.0009835603 0.02614840
## 62 62 0.03199807 0.2229881 0.02450440 0.0009816007 0.02656110
## 63 63 0.03199817 0.2230134 0.02450527 0.0009905666 0.02637606
## 64 64 0.03200846 0.2225824 0.02451152 0.0009874893 0.02651540
## 65 65 0.03200332 0.2228327 0.02450448 0.0009808047 0.02673870
## 66 66 0.03200666 0.2226796 0.02451076 0.0009887496 0.02668168
## 67 67 0.03201699 0.2222241 0.02451680 0.0009906531 0.02598043
## 68 68 0.03201825 0.2222007 0.02451956 0.0009921786 0.02641773
## 69 69 0.03201962 0.2222185 0.02451188 0.0009913407 0.02686178
## 70 70 0.03203755 0.2214853 0.02452414 0.0009873443 0.02745757
## 71 71 0.03203259 0.2217402 0.02452795 0.0009952913 0.02736407
## 72 72 0.03204207 0.2213363 0.02453509 0.0009952307 0.02784528
## 73 73 0.03205438 0.2208065 0.02453749 0.0010010351 0.02782146
## 74 74 0.03205200 0.2209117 0.02453727 0.0009891835 0.02747961
## 75 75 0.03206266 0.2204415 0.02453910 0.0009876219 0.02745146
## 76 76 0.03206407 0.2204362 0.02453660 0.0009879116 0.02707874
## 77 77 0.03206083 0.2205941 0.02453477 0.0009846399 0.02737700
## 78 78 0.03207119 0.2201542 0.02454253 0.0009803147 0.02697466
## 79 79 0.03207536 0.2199857 0.02454560 0.0009776743 0.02712203
## 80 80 0.03207472 0.2200595 0.02454699 0.0009820153 0.02771544
## 81 81 0.03208023 0.2198302 0.02455382 0.0009905057 0.02763422
## 82 82 0.03208116 0.2198268 0.02455111 0.0009886707 0.02809662
## 83 83 0.03208485 0.2196365 0.02454795 0.0009946036 0.02777544
## 84 84 0.03208038 0.2198009 0.02454448 0.0009946813 0.02758717
## 85 85 0.03208472 0.2196182 0.02455277 0.0009938144 0.02799061
## 86 86 0.03208625 0.2195444 0.02455738 0.0009914175 0.02766385
## 87 87 0.03209925 0.2189983 0.02456521 0.0009825900 0.02791736
## 88 88 0.03209417 0.2192246 0.02456524 0.0009803226 0.02778533
## 89 89 0.03209894 0.2190303 0.02456644 0.0009754795 0.02787744
## 90 90 0.03209838 0.2191245 0.02456505 0.0009819499 0.02800324
## 91 91 0.03209825 0.2191558 0.02456419 0.0009833084 0.02778367
## 92 92 0.03210400 0.2189652 0.02456694 0.0009856553 0.02812231
## 93 93 0.03210434 0.2189801 0.02457084 0.0009801447 0.02823996
## 94 94 0.03210060 0.2191943 0.02456772 0.0009654844 0.02819118
## 95 95 0.03210529 0.2190246 0.02457198 0.0009636203 0.02831531
## 96 96 0.03210450 0.2190624 0.02457628 0.0009700206 0.02832323
## 97 97 0.03209898 0.2192976 0.02457105 0.0009694717 0.02822326
## 98 98 0.03210560 0.2190003 0.02457457 0.0009800695 0.02833946
## 99 99 0.03211869 0.2184357 0.02458472 0.0009773209 0.02789135
## 100 100 0.03212527 0.2181999 0.02458565 0.0009787023 0.02804229
## 101 101 0.03212285 0.2183577 0.02458330 0.0009766513 0.02831127
## 102 102 0.03212769 0.2181718 0.02458644 0.0009780267 0.02795472
## 103 103 0.03212853 0.2181778 0.02459237 0.0009802766 0.02799265
## 104 104 0.03213054 0.2181329 0.02459439 0.0009807890 0.02792203
## 105 105 0.03212531 0.2183903 0.02459002 0.0009831817 0.02779635
## 106 106 0.03212718 0.2183151 0.02458924 0.0009909548 0.02796117
## 107 107 0.03213148 0.2181438 0.02459588 0.0009866200 0.02784121
## 108 108 0.03212901 0.2182526 0.02459664 0.0009872206 0.02797870
## 109 109 0.03214069 0.2177782 0.02460351 0.0009937091 0.02772076
## 110 110 0.03214189 0.2177448 0.02460644 0.0009939408 0.02807672
## 111 111 0.03214224 0.2177197 0.02460623 0.0009903644 0.02810488
## 112 112 0.03214342 0.2176613 0.02460782 0.0009970808 0.02800171
## 113 113 0.03214129 0.2177585 0.02460478 0.0009923991 0.02814922
## 114 114 0.03214099 0.2177672 0.02460829 0.0009898204 0.02794168
## 115 115 0.03214447 0.2176380 0.02461003 0.0009822946 0.02809847
## 116 116 0.03214458 0.2176760 0.02461034 0.0009887367 0.02799482
## 117 117 0.03215664 0.2171590 0.02462468 0.0009890154 0.02781091
## 118 118 0.03216167 0.2169618 0.02462651 0.0009924178 0.02824895
## 119 119 0.03215972 0.2170612 0.02462358 0.0009974675 0.02829916
## 120 120 0.03216148 0.2169841 0.02462209 0.0010072158 0.02835169
## 121 121 0.03216677 0.2167314 0.02462646 0.0010086517 0.02820218
## 122 122 0.03217018 0.2165822 0.02462492 0.0009989982 0.02813177
## 123 123 0.03217773 0.2162525 0.02462955 0.0009983265 0.02778428
## 124 124 0.03218328 0.2160552 0.02463655 0.0009976690 0.02782595
## 125 125 0.03217901 0.2162559 0.02463380 0.0009963381 0.02805329
## 126 126 0.03217317 0.2165350 0.02463161 0.0009898299 0.02798105
## 127 127 0.03216966 0.2166961 0.02462959 0.0009920346 0.02769300
## 128 128 0.03217511 0.2164790 0.02463733 0.0009920044 0.02767984
## 129 129 0.03217196 0.2165960 0.02463630 0.0009978645 0.02759374
## 130 130 0.03217441 0.2165134 0.02463715 0.0010006729 0.02762871
## 131 131 0.03218228 0.2161996 0.02464428 0.0009990523 0.02764652
## 132 132 0.03218117 0.2162294 0.02464626 0.0010039255 0.02758428
## 133 133 0.03218240 0.2161854 0.02464686 0.0010014319 0.02754837
## 134 134 0.03218657 0.2160732 0.02464735 0.0010026138 0.02765308
## 135 135 0.03218363 0.2162047 0.02464800 0.0010007020 0.02763048
## 136 136 0.03218603 0.2161304 0.02464921 0.0010081763 0.02742975
## 137 137 0.03217995 0.2163820 0.02464368 0.0010044814 0.02735719
## 138 138 0.03217741 0.2165008 0.02463995 0.0010039257 0.02717285
## 139 139 0.03217691 0.2165094 0.02463613 0.0010079211 0.02701616
## 140 140 0.03218028 0.2163764 0.02463569 0.0010124188 0.02692202
## 141 141 0.03218624 0.2161521 0.02464017 0.0010125825 0.02688385
## 142 142 0.03219537 0.2157547 0.02465103 0.0010143060 0.02682086
## 143 143 0.03220021 0.2155697 0.02465256 0.0010121680 0.02685285
## 144 144 0.03220340 0.2154408 0.02465567 0.0010086419 0.02696587
## 145 145 0.03220323 0.2154612 0.02465466 0.0010072197 0.02712649
## 146 146 0.03220615 0.2153383 0.02465568 0.0010111334 0.02726330
## 147 147 0.03220933 0.2152114 0.02465549 0.0010130228 0.02711207
## 148 148 0.03221524 0.2149885 0.02465565 0.0010161915 0.02738923
## 149 149 0.03221353 0.2150468 0.02465379 0.0010243199 0.02723500
## 150 150 0.03221589 0.2149345 0.02465345 0.0010275719 0.02697666
## 151 151 0.03221819 0.2148270 0.02465558 0.0010292177 0.02714007
## 152 152 0.03221929 0.2147485 0.02465630 0.0010293177 0.02696511
## 153 153 0.03221991 0.2147198 0.02465699 0.0010335789 0.02687222
## 154 154 0.03222420 0.2145461 0.02465916 0.0010310222 0.02691348
## 155 155 0.03222711 0.2144446 0.02466218 0.0010266696 0.02712656
## 156 156 0.03223619 0.2140530 0.02466660 0.0010286898 0.02717952
## 157 157 0.03224181 0.2137987 0.02466921 0.0010306819 0.02700361
## 158 158 0.03223705 0.2140214 0.02466559 0.0010289106 0.02696976
## 159 159 0.03223360 0.2141595 0.02466473 0.0010339439 0.02695353
## 160 160 0.03223173 0.2142344 0.02466349 0.0010352421 0.02686554
## 161 161 0.03223019 0.2143146 0.02466177 0.0010354535 0.02700547
## 162 162 0.03222553 0.2145163 0.02465750 0.0010336107 0.02704354
## 163 163 0.03222476 0.2145464 0.02465840 0.0010363876 0.02698129
## 164 164 0.03222191 0.2146655 0.02465443 0.0010310510 0.02702169
## 165 165 0.03221676 0.2148991 0.02465257 0.0010286564 0.02696774
## 166 166 0.03221585 0.2149465 0.02464896 0.0010229804 0.02697569
## 167 167 0.03221910 0.2148206 0.02465297 0.0010262417 0.02689212
## 168 168 0.03221552 0.2149850 0.02465197 0.0010291154 0.02706303
## 169 169 0.03222021 0.2147986 0.02465958 0.0010273574 0.02710324
## 170 170 0.03222106 0.2147695 0.02466060 0.0010273249 0.02718464
## 171 171 0.03222330 0.2146761 0.02466399 0.0010277685 0.02701815
## 172 172 0.03222364 0.2146640 0.02466646 0.0010256659 0.02700205
## 173 173 0.03222302 0.2147088 0.02466638 0.0010265446 0.02689653
## 174 174 0.03222351 0.2146887 0.02466643 0.0010286469 0.02676358
## 175 175 0.03222466 0.2146571 0.02466432 0.0010316099 0.02681563
## 176 176 0.03222912 0.2144594 0.02466978 0.0010341659 0.02671964
## 177 177 0.03223220 0.2143265 0.02467385 0.0010340666 0.02670874
## 178 178 0.03223165 0.2143418 0.02467448 0.0010318501 0.02664083
## 179 179 0.03223126 0.2143676 0.02467398 0.0010312085 0.02673380
## 180 180 0.03223281 0.2143009 0.02467374 0.0010314201 0.02686293
## 181 181 0.03223690 0.2141224 0.02467733 0.0010299603 0.02691721
## 182 182 0.03223190 0.2143298 0.02467426 0.0010312906 0.02701837
## 183 183 0.03222869 0.2144640 0.02467309 0.0010336079 0.02705171
## 184 184 0.03223128 0.2143541 0.02467655 0.0010369733 0.02715954
## 185 185 0.03222449 0.2146414 0.02467279 0.0010377651 0.02720021
## 186 186 0.03222232 0.2147351 0.02467095 0.0010366830 0.02717686
## 187 187 0.03222255 0.2147256 0.02467203 0.0010366656 0.02726421
## 188 188 0.03222582 0.2145900 0.02467648 0.0010381219 0.02727544
## 189 189 0.03222725 0.2145114 0.02467832 0.0010422847 0.02720718
## 190 190 0.03222934 0.2144297 0.02468033 0.0010440887 0.02729145
## 191 191 0.03222926 0.2144298 0.02467897 0.0010433988 0.02734322
## 192 192 0.03222938 0.2144212 0.02467963 0.0010441770 0.02744350
## 193 193 0.03223054 0.2143701 0.02468088 0.0010438126 0.02745762
## 194 194 0.03223178 0.2143165 0.02468278 0.0010406123 0.02738261
## 195 195 0.03223065 0.2143718 0.02468234 0.0010400593 0.02741552
## 196 196 0.03222926 0.2144278 0.02468114 0.0010404049 0.02750016
## 197 197 0.03222897 0.2144278 0.02468163 0.0010440271 0.02745283
## 198 198 0.03223127 0.2143341 0.02468367 0.0010440249 0.02753839
## 199 199 0.03223049 0.2143638 0.02468279 0.0010439267 0.02750348
## 200 200 0.03223011 0.2143799 0.02468214 0.0010477634 0.02755269
## 201 201 0.03222937 0.2144093 0.02468108 0.0010476185 0.02764257
## 202 202 0.03223170 0.2143143 0.02468308 0.0010448119 0.02759767
## 203 203 0.03222986 0.2143986 0.02468219 0.0010429669 0.02758904
## 204 204 0.03223128 0.2143422 0.02468494 0.0010437813 0.02748991
## 205 205 0.03223160 0.2143291 0.02468513 0.0010444590 0.02757676
## 206 206 0.03223155 0.2143227 0.02468628 0.0010464766 0.02756180
## 207 207 0.03223262 0.2142770 0.02468709 0.0010461715 0.02757732
## 208 208 0.03223266 0.2142798 0.02468622 0.0010455724 0.02765699
## 209 209 0.03223301 0.2142686 0.02468618 0.0010443455 0.02762081
## 210 210 0.03223320 0.2142497 0.02468694 0.0010465787 0.02753182
## 211 211 0.03223318 0.2142541 0.02468825 0.0010473600 0.02750010
## 212 212 0.03223250 0.2142915 0.02468787 0.0010461387 0.02754856
## 213 213 0.03223343 0.2142513 0.02468865 0.0010461559 0.02751035
## 214 214 0.03223389 0.2142325 0.02468867 0.0010437209 0.02746146
## 215 215 0.03223403 0.2142238 0.02468962 0.0010456569 0.02733998
## 216 216 0.03223479 0.2141985 0.02469043 0.0010441398 0.02729859
## 217 217 0.03223538 0.2141725 0.02469075 0.0010437772 0.02732615
## 218 218 0.03223540 0.2141690 0.02469092 0.0010442496 0.02728024
## 219 219 0.03223475 0.2142005 0.02469020 0.0010446956 0.02728845
## 220 220 0.03223428 0.2142236 0.02468977 0.0010444562 0.02725898
## 221 221 0.03223504 0.2141853 0.02469117 0.0010445416 0.02720648
## 222 222 0.03223526 0.2141694 0.02469290 0.0010456283 0.02720763
## 223 223 0.03223507 0.2141796 0.02469298 0.0010452188 0.02726817
## 224 224 0.03223452 0.2142026 0.02469286 0.0010448990 0.02725018
## 225 225 0.03223502 0.2141819 0.02469298 0.0010445208 0.02725529
## 226 226 0.03223463 0.2141981 0.02469295 0.0010447328 0.02723686
## 227 227 0.03223413 0.2142199 0.02469274 0.0010449003 0.02723661
## 228 228 0.03223320 0.2142582 0.02469156 0.0010457992 0.02721714
## 229 229 0.03223376 0.2142368 0.02469243 0.0010446661 0.02720360
## 230 230 0.03223402 0.2142238 0.02469267 0.0010448681 0.02719640
## 231 231 0.03223373 0.2142371 0.02469240 0.0010450460 0.02721503
## 232 232 0.03223404 0.2142241 0.02469243 0.0010453876 0.02723733
## 233 233 0.03223429 0.2142132 0.02469274 0.0010456495 0.02726316
## 234 234 0.03223392 0.2142269 0.02469274 0.0010457886 0.02725864
## 235 235 0.03223414 0.2142182 0.02469299 0.0010459490 0.02725502
## 236 236 0.03223406 0.2142203 0.02469294 0.0010461860 0.02728085
## 237 237 0.03223403 0.2142210 0.02469284 0.0010459806 0.02725711
## 238 238 0.03223436 0.2142079 0.02469323 0.0010461352 0.02727336
## 239 239 0.03223419 0.2142160 0.02469319 0.0010460914 0.02727348
## 240 240 0.03223413 0.2142186 0.02469321 0.0010461107 0.02727501
## MAESD
## 1 0.0006039987
## 2 0.0005560515
## 3 0.0005161705
## 4 0.0005030416
## 5 0.0004516171
## 6 0.0004274403
## 7 0.0004386333
## 8 0.0004116784
## 9 0.0004027648
## 10 0.0003804190
## 11 0.0003827175
## 12 0.0003714371
## 13 0.0003683846
## 14 0.0003455862
## 15 0.0003658994
## 16 0.0003688301
## 17 0.0003957368
## 18 0.0004141335
## 19 0.0003935137
## 20 0.0004296221
## 21 0.0004303736
## 22 0.0004185360
## 23 0.0004078318
## 24 0.0004093406
## 25 0.0004080051
## 26 0.0004010876
## 27 0.0003913574
## 28 0.0003946936
## 29 0.0004037086
## 30 0.0004107282
## 31 0.0004226944
## 32 0.0004225034
## 33 0.0004448925
## 34 0.0004395554
## 35 0.0004402790
## 36 0.0004361420
## 37 0.0004482860
## 38 0.0004605232
## 39 0.0004645625
## 40 0.0004664103
## 41 0.0004825004
## 42 0.0004953591
## 43 0.0004961167
## 44 0.0004970095
## 45 0.0004823461
## 46 0.0004800355
## 47 0.0004673817
## 48 0.0004671513
## 49 0.0004575740
## 50 0.0004538221
## 51 0.0004545868
## 52 0.0004697742
## 53 0.0004648600
## 54 0.0004673599
## 55 0.0004620623
## 56 0.0004669444
## 57 0.0004512827
## 58 0.0004522348
## 59 0.0004465430
## 60 0.0004446658
## 61 0.0004252333
## 62 0.0004267543
## 63 0.0004375628
## 64 0.0004362941
## 65 0.0004314322
## 66 0.0004333280
## 67 0.0004301794
## 68 0.0004320149
## 69 0.0004320135
## 70 0.0004455633
## 71 0.0004521234
## 72 0.0004604000
## 73 0.0004654286
## 74 0.0004539451
## 75 0.0004571830
## 76 0.0004519315
## 77 0.0004484431
## 78 0.0004450123
## 79 0.0004611947
## 80 0.0004803766
## 81 0.0004789181
## 82 0.0004805474
## 83 0.0004796394
## 84 0.0004781293
## 85 0.0004735814
## 86 0.0004631454
## 87 0.0004591645
## 88 0.0004536634
## 89 0.0004457315
## 90 0.0004525686
## 91 0.0004540103
## 92 0.0004584299
## 93 0.0004567965
## 94 0.0004435839
## 95 0.0004467587
## 96 0.0004418337
## 97 0.0004563701
## 98 0.0004690919
## 99 0.0004598628
## 100 0.0004640794
## 101 0.0004640015
## 102 0.0004560496
## 103 0.0004648776
## 104 0.0004599889
## 105 0.0004620722
## 106 0.0004633503
## 107 0.0004554726
## 108 0.0004541538
## 109 0.0004492258
## 110 0.0004458360
## 111 0.0004461623
## 112 0.0004503142
## 113 0.0004449964
## 114 0.0004438039
## 115 0.0004373721
## 116 0.0004417682
## 117 0.0004388079
## 118 0.0004509588
## 119 0.0004527550
## 120 0.0004564964
## 121 0.0004607525
## 122 0.0004530242
## 123 0.0004445684
## 124 0.0004437379
## 125 0.0004428616
## 126 0.0004383164
## 127 0.0004439654
## 128 0.0004425114
## 129 0.0004475278
## 130 0.0004539906
## 131 0.0004555961
## 132 0.0004548395
## 133 0.0004500750
## 134 0.0004548076
## 135 0.0004505811
## 136 0.0004532243
## 137 0.0004523636
## 138 0.0004523215
## 139 0.0004536599
## 140 0.0004508349
## 141 0.0004520956
## 142 0.0004529830
## 143 0.0004506338
## 144 0.0004482863
## 145 0.0004452007
## 146 0.0004465150
## 147 0.0004506502
## 148 0.0004551106
## 149 0.0004572718
## 150 0.0004541435
## 151 0.0004544823
## 152 0.0004551410
## 153 0.0004611971
## 154 0.0004588877
## 155 0.0004558186
## 156 0.0004526859
## 157 0.0004483682
## 158 0.0004470474
## 159 0.0004521961
## 160 0.0004551061
## 161 0.0004572308
## 162 0.0004519747
## 163 0.0004539580
## 164 0.0004492109
## 165 0.0004472587
## 166 0.0004416734
## 167 0.0004462159
## 168 0.0004485969
## 169 0.0004460821
## 170 0.0004482488
## 171 0.0004496480
## 172 0.0004542897
## 173 0.0004531522
## 174 0.0004527769
## 175 0.0004524037
## 176 0.0004526027
## 177 0.0004543979
## 178 0.0004540366
## 179 0.0004554637
## 180 0.0004566642
## 181 0.0004545913
## 182 0.0004598192
## 183 0.0004621286
## 184 0.0004650437
## 185 0.0004664027
## 186 0.0004664385
## 187 0.0004679595
## 188 0.0004653387
## 189 0.0004674643
## 190 0.0004709530
## 191 0.0004717812
## 192 0.0004715297
## 193 0.0004726529
## 194 0.0004715795
## 195 0.0004698748
## 196 0.0004716496
## 197 0.0004727940
## 198 0.0004731631
## 199 0.0004723402
## 200 0.0004763508
## 201 0.0004749317
## 202 0.0004731103
## 203 0.0004717088
## 204 0.0004715263
## 205 0.0004730446
## 206 0.0004730972
## 207 0.0004722764
## 208 0.0004738190
## 209 0.0004720531
## 210 0.0004721833
## 211 0.0004724902
## 212 0.0004712165
## 213 0.0004703265
## 214 0.0004683633
## 215 0.0004682378
## 216 0.0004670981
## 217 0.0004664416
## 218 0.0004662273
## 219 0.0004660352
## 220 0.0004662158
## 221 0.0004657934
## 222 0.0004665774
## 223 0.0004669865
## 224 0.0004666995
## 225 0.0004664618
## 226 0.0004665780
## 227 0.0004670497
## 228 0.0004673052
## 229 0.0004660760
## 230 0.0004663063
## 231 0.0004664695
## 232 0.0004670043
## 233 0.0004673417
## 234 0.0004674003
## 235 0.0004671664
## 236 0.0004674086
## 237 0.0004670385
## 238 0.0004674181
## 239 0.0004674932
## 240 0.0004675061
## nvmax
## 21 21
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Coefficients of final model:
## (Intercept) x4 x7 x8 x9
## 1.968225e+00 -4.615460e-05 1.126211e-02 5.378319e-04 3.521503e-03
## x10 x11 x16 x17 x21
## 1.089648e-03 1.907896e+05 8.000674e-04 1.492599e-03 1.314116e-04
## stat14 stat23 stat60 stat98 stat104
## -7.007932e-04 7.363328e-04 6.162420e-04 3.486509e-03 -5.752055e-04
## stat110 stat144 stat149 stat187 stat198
## -3.348495e-03 6.801916e-04 -6.903981e-04 -6.306481e-04 -5.287456e-04
## stat204 sqrt.x18
## -5.279347e-04 2.669560e-02Test
if (algo.backward.caret == TRUE){
test.model(model.backward, data.test
,method = 'leapBackward',subopt = NULL
,formula = formula, feature.names = feature.names, label.names = label.names
,id = id
,draw.limits = TRUE, transformation = t)
}## [1] "Summary of predicted values: "
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.034 2.084 2.097 2.096 2.109 2.147
## [1] "leapBackward Test MSE: 0.00103959607798376"
Backward Elimination with CV (w/ filtered train)
Train
if (algo.backward.caret == TRUE){
set.seed(1)
returned = train.caret.glmselect(formula = formula
,data = data.train2
,method = "leapBackward"
,feature.names = feature.names)
model.backward = returned$model
id = returned$id
}## Aggregating results
## Selecting tuning parameters
## Fitting nvmax = 17 on full training set
## nvmax RMSE Rsquared MAE RMSESD RsquaredSD
## 1 1 0.02888038 0.1564097 0.02335547 0.0006267618 0.02633141
## 2 2 0.02766787 0.2253658 0.02249776 0.0006867294 0.02103652
## 3 3 0.02710230 0.2568330 0.02192148 0.0008105304 0.02659149
## 4 4 0.02638810 0.2952697 0.02113655 0.0007311698 0.02534274
## 5 5 0.02604900 0.3130434 0.02090228 0.0006875295 0.02296316
## 6 6 0.02593917 0.3187854 0.02084831 0.0006494856 0.02073056
## 7 7 0.02589964 0.3209157 0.02084970 0.0006015737 0.02042737
## 8 8 0.02582820 0.3246426 0.02082087 0.0005550767 0.02247168
## 9 9 0.02579308 0.3265747 0.02082103 0.0005507879 0.02199516
## 10 10 0.02571588 0.3305729 0.02078122 0.0005582145 0.02228794
## 11 11 0.02568025 0.3325064 0.02074574 0.0005288941 0.02225932
## 12 12 0.02567812 0.3326191 0.02075803 0.0005158039 0.02239029
## 13 13 0.02564510 0.3343325 0.02073002 0.0005334347 0.02150174
## 14 14 0.02565553 0.3337725 0.02074496 0.0005376892 0.02133207
## 15 15 0.02565558 0.3337869 0.02074086 0.0005389872 0.02180600
## 16 16 0.02565815 0.3336140 0.02074875 0.0005288357 0.02226318
## 17 17 0.02563966 0.3346075 0.02075470 0.0005273379 0.02198116
## 18 18 0.02567450 0.3328184 0.02077682 0.0005577289 0.02193368
## 19 19 0.02567251 0.3329551 0.02077497 0.0005421905 0.02160312
## 20 20 0.02570569 0.3313067 0.02079962 0.0005573473 0.02263287
## 21 21 0.02572145 0.3305660 0.02081432 0.0005350083 0.02107574
## 22 22 0.02571745 0.3308431 0.02080336 0.0005253332 0.02118837
## 23 23 0.02572432 0.3304617 0.02080958 0.0005122830 0.02142865
## 24 24 0.02572928 0.3302298 0.02080454 0.0005156292 0.02249707
## 25 25 0.02573962 0.3297634 0.02080145 0.0005076975 0.02220226
## 26 26 0.02571756 0.3308208 0.02078905 0.0005283183 0.02295738
## 27 27 0.02571703 0.3308756 0.02077874 0.0005229586 0.02378530
## 28 28 0.02571244 0.3311394 0.02076828 0.0005201583 0.02332965
## 29 29 0.02571695 0.3309301 0.02077867 0.0005233092 0.02305921
## 30 30 0.02569731 0.3319392 0.02076254 0.0005228247 0.02225371
## 31 31 0.02569099 0.3322713 0.02076104 0.0005335996 0.02320756
## 32 32 0.02568091 0.3327698 0.02073825 0.0005178291 0.02276693
## 33 33 0.02569096 0.3322634 0.02074979 0.0005145192 0.02236245
## 34 34 0.02567636 0.3330014 0.02074480 0.0005144960 0.02229300
## 35 35 0.02567983 0.3328387 0.02074383 0.0005094589 0.02335017
## 36 36 0.02568686 0.3325299 0.02073976 0.0005056167 0.02344333
## 37 37 0.02568570 0.3326016 0.02074075 0.0005286131 0.02277088
## 38 38 0.02568638 0.3326004 0.02074278 0.0005350692 0.02260502
## 39 39 0.02568286 0.3327595 0.02073485 0.0005287754 0.02230007
## 40 40 0.02568454 0.3327109 0.02073789 0.0005365565 0.02306433
## 41 41 0.02567329 0.3333159 0.02073435 0.0005397255 0.02322542
## 42 42 0.02567772 0.3330906 0.02074146 0.0005350801 0.02269924
## 43 43 0.02566275 0.3338622 0.02073982 0.0005462443 0.02243724
## 44 44 0.02567082 0.3335029 0.02074629 0.0005575857 0.02289903
## 45 45 0.02568473 0.3328624 0.02075953 0.0005595466 0.02341015
## 46 46 0.02569083 0.3326059 0.02076323 0.0005617568 0.02375068
## 47 47 0.02569946 0.3321807 0.02077729 0.0005461298 0.02312327
## 48 48 0.02570694 0.3318366 0.02078093 0.0005608808 0.02398227
## 49 49 0.02570771 0.3317834 0.02079036 0.0005708561 0.02463267
## 50 50 0.02571639 0.3313935 0.02080577 0.0005848652 0.02485342
## 51 51 0.02571066 0.3316740 0.02080134 0.0005812832 0.02520493
## 52 52 0.02571331 0.3315796 0.02079779 0.0005551366 0.02451314
## 53 53 0.02570849 0.3318281 0.02080063 0.0005558138 0.02491113
## 54 54 0.02570599 0.3319673 0.02079482 0.0005546238 0.02485081
## 55 55 0.02570246 0.3321582 0.02078976 0.0005514278 0.02461288
## 56 56 0.02570325 0.3321666 0.02078350 0.0005498309 0.02476050
## 57 57 0.02572034 0.3313728 0.02079365 0.0005534066 0.02533999
## 58 58 0.02572871 0.3309725 0.02080136 0.0005604899 0.02561695
## 59 59 0.02572404 0.3312330 0.02079534 0.0005592884 0.02547807
## 60 60 0.02573404 0.3307269 0.02081168 0.0005618054 0.02569169
## 61 61 0.02573608 0.3306454 0.02080592 0.0005563158 0.02538104
## 62 62 0.02575237 0.3298857 0.02081633 0.0005819762 0.02569464
## 63 63 0.02575054 0.3300064 0.02081864 0.0005803455 0.02543575
## 64 64 0.02575167 0.3299783 0.02082416 0.0005815590 0.02518551
## 65 65 0.02574712 0.3302708 0.02082071 0.0005866952 0.02541418
## 66 66 0.02574612 0.3303458 0.02081968 0.0005979830 0.02526880
## 67 67 0.02575563 0.3298858 0.02082248 0.0006002045 0.02579809
## 68 68 0.02574719 0.3303432 0.02081677 0.0005964441 0.02558502
## 69 69 0.02575625 0.3299513 0.02082526 0.0005982113 0.02548721
## 70 70 0.02576944 0.3293312 0.02083566 0.0006005036 0.02534062
## 71 71 0.02576537 0.3295151 0.02083234 0.0005968164 0.02492351
## 72 72 0.02577316 0.3291135 0.02083680 0.0005939839 0.02487979
## 73 73 0.02578598 0.3285133 0.02084649 0.0005947746 0.02473150
## 74 74 0.02579079 0.3282821 0.02085145 0.0005992973 0.02476445
## 75 75 0.02580140 0.3277739 0.02085912 0.0006020817 0.02458598
## 76 76 0.02580887 0.3274361 0.02086250 0.0006061824 0.02478010
## 77 77 0.02582297 0.3267324 0.02087888 0.0006042877 0.02442560
## 78 78 0.02582579 0.3266021 0.02088487 0.0005979411 0.02389879
## 79 79 0.02582967 0.3264132 0.02088885 0.0005902857 0.02340927
## 80 80 0.02584264 0.3257784 0.02090073 0.0005811177 0.02285018
## 81 81 0.02584181 0.3258403 0.02089819 0.0005832150 0.02282523
## 82 82 0.02585085 0.3254429 0.02090735 0.0005833348 0.02308655
## 83 83 0.02584393 0.3257886 0.02090389 0.0005883268 0.02336236
## 84 84 0.02584308 0.3258459 0.02090300 0.0005910379 0.02309422
## 85 85 0.02583660 0.3261900 0.02089235 0.0005867458 0.02304421
## 86 86 0.02582759 0.3266669 0.02088669 0.0005844546 0.02324747
## 87 87 0.02583165 0.3265219 0.02088934 0.0005748188 0.02283684
## 88 88 0.02582447 0.3269211 0.02088201 0.0005825564 0.02323083
## 89 89 0.02582379 0.3269877 0.02088347 0.0005791658 0.02326645
## 90 90 0.02581768 0.3272874 0.02087990 0.0005795068 0.02311229
## 91 91 0.02581858 0.3272141 0.02087979 0.0005714053 0.02275776
## 92 92 0.02582065 0.3271103 0.02088160 0.0005699833 0.02242137
## 93 93 0.02582024 0.3271564 0.02088697 0.0005674436 0.02241051
## 94 94 0.02582425 0.3269777 0.02089474 0.0005608798 0.02218480
## 95 95 0.02582704 0.3268653 0.02090146 0.0005691854 0.02229040
## 96 96 0.02583780 0.3263118 0.02091120 0.0005709819 0.02275849
## 97 97 0.02583194 0.3265821 0.02091205 0.0005627072 0.02245509
## 98 98 0.02583563 0.3264118 0.02091055 0.0005684523 0.02285248
## 99 99 0.02583277 0.3265603 0.02091241 0.0005807993 0.02295389
## 100 100 0.02583383 0.3265458 0.02091071 0.0005793584 0.02313779
## 101 101 0.02583152 0.3266299 0.02090691 0.0005752158 0.02306261
## 102 102 0.02583074 0.3266810 0.02090676 0.0005730749 0.02271006
## 103 103 0.02583064 0.3266946 0.02090250 0.0005764494 0.02302662
## 104 104 0.02582792 0.3268429 0.02089906 0.0005712405 0.02271117
## 105 105 0.02582193 0.3271517 0.02089296 0.0005760601 0.02286384
## 106 106 0.02582338 0.3270559 0.02089953 0.0005810822 0.02317164
## 107 107 0.02582498 0.3269811 0.02090311 0.0005912515 0.02360844
## 108 108 0.02582454 0.3270319 0.02090503 0.0005928183 0.02388058
## 109 109 0.02582773 0.3268964 0.02091437 0.0005952162 0.02391593
## 110 110 0.02581830 0.3273394 0.02091078 0.0005982447 0.02367525
## 111 111 0.02581400 0.3275396 0.02090208 0.0005971839 0.02355243
## 112 112 0.02580871 0.3277836 0.02089724 0.0005846499 0.02305313
## 113 113 0.02580692 0.3278690 0.02089457 0.0005874713 0.02305678
## 114 114 0.02579791 0.3283203 0.02089060 0.0005846388 0.02306257
## 115 115 0.02579864 0.3282889 0.02089189 0.0005832197 0.02332018
## 116 116 0.02579828 0.3283214 0.02089638 0.0005832395 0.02302931
## 117 117 0.02580671 0.3279436 0.02089777 0.0005921621 0.02303747
## 118 118 0.02580713 0.3279377 0.02089839 0.0005936209 0.02324419
## 119 119 0.02580935 0.3278339 0.02089990 0.0005937796 0.02316200
## 120 120 0.02580867 0.3278473 0.02090396 0.0005962684 0.02299158
## 121 121 0.02580583 0.3280010 0.02090000 0.0005969021 0.02289421
## 122 122 0.02581565 0.3275327 0.02090558 0.0006021020 0.02313400
## 123 123 0.02581667 0.3274748 0.02090347 0.0005963106 0.02304517
## 124 124 0.02581341 0.3276391 0.02089850 0.0005962448 0.02297065
## 125 125 0.02581641 0.3275032 0.02090312 0.0005934410 0.02299640
## 126 126 0.02581309 0.3276502 0.02090341 0.0005937065 0.02276878
## 127 127 0.02581618 0.3274976 0.02090291 0.0005903338 0.02286409
## 128 128 0.02582193 0.3272085 0.02090656 0.0005954018 0.02290651
## 129 129 0.02582484 0.3270686 0.02090478 0.0005912943 0.02286998
## 130 130 0.02582403 0.3271122 0.02090698 0.0005958074 0.02300095
## 131 131 0.02582145 0.3272323 0.02090141 0.0006002880 0.02360411
## 132 132 0.02581396 0.3275983 0.02089774 0.0005963332 0.02356754
## 133 133 0.02581792 0.3274085 0.02089967 0.0005991808 0.02356760
## 134 134 0.02581567 0.3275245 0.02089735 0.0005962420 0.02348222
## 135 135 0.02580863 0.3278732 0.02089413 0.0006065294 0.02400052
## 136 136 0.02580276 0.3281427 0.02088961 0.0006040719 0.02372495
## 137 137 0.02579986 0.3282852 0.02088501 0.0006058636 0.02367396
## 138 138 0.02579827 0.3283987 0.02088374 0.0006063617 0.02353857
## 139 139 0.02579853 0.3283549 0.02088201 0.0006119529 0.02391112
## 140 140 0.02580245 0.3281660 0.02088657 0.0006140684 0.02408106
## 141 141 0.02580331 0.3281539 0.02088633 0.0006152898 0.02405010
## 142 142 0.02580859 0.3279062 0.02088840 0.0006114647 0.02382492
## 143 143 0.02581420 0.3276530 0.02089250 0.0006121345 0.02402210
## 144 144 0.02581371 0.3276844 0.02089098 0.0006126948 0.02391850
## 145 145 0.02581099 0.3278022 0.02088740 0.0006147775 0.02392746
## 146 146 0.02580886 0.3279055 0.02088500 0.0006091901 0.02385621
## 147 147 0.02580716 0.3279902 0.02088560 0.0006085836 0.02382285
## 148 148 0.02580070 0.3283251 0.02088325 0.0006057108 0.02387619
## 149 149 0.02580521 0.3281216 0.02088507 0.0006080115 0.02387607
## 150 150 0.02580528 0.3281351 0.02088574 0.0006084479 0.02386306
## 151 151 0.02580714 0.3280483 0.02088838 0.0006055670 0.02365136
## 152 152 0.02580870 0.3279982 0.02089148 0.0006037103 0.02337011
## 153 153 0.02581028 0.3279218 0.02089359 0.0006028604 0.02341335
## 154 154 0.02580537 0.3281610 0.02089001 0.0006047494 0.02340353
## 155 155 0.02580457 0.3281934 0.02088879 0.0006072620 0.02326140
## 156 156 0.02580915 0.3279849 0.02088981 0.0006000412 0.02305671
## 157 157 0.02580601 0.3281527 0.02088919 0.0005996353 0.02294202
## 158 158 0.02579702 0.3285738 0.02087969 0.0006018117 0.02300721
## 159 159 0.02579981 0.3284511 0.02088321 0.0006071578 0.02320003
## 160 160 0.02579370 0.3287494 0.02087794 0.0006074290 0.02320654
## 161 161 0.02579114 0.3288656 0.02087361 0.0006052544 0.02329639
## 162 162 0.02579552 0.3286747 0.02087494 0.0005993793 0.02312993
## 163 163 0.02579294 0.3287792 0.02087085 0.0006010475 0.02329765
## 164 164 0.02578822 0.3290137 0.02086440 0.0006019867 0.02347243
## 165 165 0.02578609 0.3291135 0.02086158 0.0005981495 0.02350725
## 166 166 0.02578558 0.3291366 0.02086110 0.0005994136 0.02370887
## 167 167 0.02578568 0.3291302 0.02086177 0.0005979542 0.02379997
## 168 168 0.02578841 0.3289942 0.02086653 0.0005970647 0.02375210
## 169 169 0.02578877 0.3289777 0.02087010 0.0005957025 0.02351222
## 170 170 0.02578689 0.3290633 0.02086977 0.0005961564 0.02342855
## 171 171 0.02578683 0.3290835 0.02086831 0.0005931100 0.02289322
## 172 172 0.02578665 0.3290948 0.02086843 0.0005953732 0.02284599
## 173 173 0.02578405 0.3292163 0.02086701 0.0005959394 0.02301613
## 174 174 0.02578408 0.3292187 0.02086561 0.0005949276 0.02298360
## 175 175 0.02578261 0.3292913 0.02086404 0.0005959604 0.02298112
## 176 176 0.02578165 0.3293326 0.02086552 0.0005917618 0.02286919
## 177 177 0.02578270 0.3292775 0.02086708 0.0005872513 0.02270647
## 178 178 0.02578725 0.3290585 0.02087211 0.0005873113 0.02276321
## 179 179 0.02578871 0.3289975 0.02087530 0.0005876232 0.02281102
## 180 180 0.02579264 0.3288038 0.02087779 0.0005823554 0.02271104
## 181 181 0.02579328 0.3287794 0.02087727 0.0005802602 0.02264810
## 182 182 0.02579484 0.3287236 0.02088082 0.0005836557 0.02264377
## 183 183 0.02579159 0.3288893 0.02087840 0.0005821803 0.02254190
## 184 184 0.02579271 0.3288355 0.02088072 0.0005819130 0.02260647
## 185 185 0.02579104 0.3289156 0.02087829 0.0005854382 0.02260853
## 186 186 0.02578973 0.3289715 0.02087898 0.0005867552 0.02255972
## 187 187 0.02579097 0.3289162 0.02087888 0.0005896168 0.02260286
## 188 188 0.02579226 0.3288658 0.02087838 0.0005863259 0.02245571
## 189 189 0.02579283 0.3288465 0.02087870 0.0005846947 0.02239217
## 190 190 0.02578956 0.3289941 0.02087719 0.0005823063 0.02226718
## 191 191 0.02578944 0.3290054 0.02087674 0.0005827214 0.02225446
## 192 192 0.02578890 0.3290219 0.02087652 0.0005838578 0.02238692
## 193 193 0.02578778 0.3290787 0.02087435 0.0005840737 0.02236546
## 194 194 0.02578982 0.3289739 0.02087471 0.0005824844 0.02239950
## 195 195 0.02578970 0.3289887 0.02087341 0.0005815862 0.02238593
## 196 196 0.02579180 0.3288837 0.02087483 0.0005846724 0.02249194
## 197 197 0.02579142 0.3289126 0.02087533 0.0005851812 0.02252236
## 198 198 0.02579054 0.3289585 0.02087409 0.0005854075 0.02249347
## 199 199 0.02578955 0.3290079 0.02087240 0.0005845142 0.02254083
## 200 200 0.02579066 0.3289553 0.02087484 0.0005845773 0.02253186
## 201 201 0.02579044 0.3289670 0.02087368 0.0005826670 0.02247715
## 202 202 0.02579222 0.3288810 0.02087633 0.0005820911 0.02232023
## 203 203 0.02579385 0.3288049 0.02087871 0.0005807395 0.02218610
## 204 204 0.02579630 0.3286833 0.02087973 0.0005824461 0.02222385
## 205 205 0.02579818 0.3285946 0.02088062 0.0005816933 0.02215222
## 206 206 0.02579794 0.3286098 0.02088030 0.0005827950 0.02226411
## 207 207 0.02579800 0.3286101 0.02088055 0.0005856948 0.02236005
## 208 208 0.02579890 0.3285743 0.02088098 0.0005839143 0.02231316
## 209 209 0.02579878 0.3285812 0.02088050 0.0005844861 0.02235314
## 210 210 0.02579904 0.3285756 0.02088019 0.0005868091 0.02241808
## 211 211 0.02579819 0.3286180 0.02087892 0.0005868832 0.02241739
## 212 212 0.02580008 0.3285284 0.02088064 0.0005852382 0.02238595
## 213 213 0.02580063 0.3285014 0.02088142 0.0005857491 0.02238421
## 214 214 0.02580032 0.3285186 0.02088129 0.0005856118 0.02240728
## 215 215 0.02579945 0.3285596 0.02087994 0.0005856497 0.02243807
## 216 216 0.02579970 0.3285507 0.02088041 0.0005865858 0.02248964
## 217 217 0.02580044 0.3285194 0.02088090 0.0005873269 0.02251864
## 218 218 0.02580116 0.3284874 0.02088125 0.0005879340 0.02253196
## 219 219 0.02580166 0.3284622 0.02088203 0.0005889135 0.02251416
## 220 220 0.02580203 0.3284464 0.02088195 0.0005886014 0.02248331
## 221 221 0.02580261 0.3284173 0.02088168 0.0005880471 0.02244028
## 222 222 0.02580418 0.3283414 0.02088338 0.0005884707 0.02243783
## 223 223 0.02580430 0.3283402 0.02088385 0.0005894817 0.02246021
## 224 224 0.02580460 0.3283266 0.02088451 0.0005893404 0.02246173
## 225 225 0.02580541 0.3282875 0.02088516 0.0005894300 0.02242379
## 226 226 0.02580533 0.3282908 0.02088529 0.0005894448 0.02244340
## 227 227 0.02580573 0.3282716 0.02088536 0.0005888277 0.02240785
## 228 228 0.02580530 0.3282953 0.02088497 0.0005896620 0.02240748
## 229 229 0.02580519 0.3282996 0.02088502 0.0005895789 0.02242132
## 230 230 0.02580500 0.3283086 0.02088452 0.0005890612 0.02242948
## 231 231 0.02580437 0.3283363 0.02088425 0.0005887867 0.02240553
## 232 232 0.02580456 0.3283292 0.02088410 0.0005887680 0.02239511
## 233 233 0.02580418 0.3283463 0.02088389 0.0005892820 0.02241764
## 234 234 0.02580336 0.3283860 0.02088309 0.0005897805 0.02244912
## 235 235 0.02580288 0.3284094 0.02088269 0.0005892806 0.02243559
## 236 236 0.02580313 0.3283991 0.02088306 0.0005894619 0.02244298
## 237 237 0.02580313 0.3283989 0.02088291 0.0005895992 0.02245239
## 238 238 0.02580317 0.3283959 0.02088292 0.0005895545 0.02245788
## 239 239 0.02580340 0.3283851 0.02088304 0.0005896238 0.02245255
## 240 240 0.02580348 0.3283813 0.02088310 0.0005896638 0.02245074
## MAESD
## 1 0.0004604171
## 2 0.0005295821
## 3 0.0005663676
## 4 0.0004831073
## 5 0.0004993662
## 6 0.0005070940
## 7 0.0004687843
## 8 0.0004502107
## 9 0.0004598547
## 10 0.0004621940
## 11 0.0004355906
## 12 0.0004338364
## 13 0.0004558980
## 14 0.0004441391
## 15 0.0004482284
## 16 0.0004294657
## 17 0.0004146737
## 18 0.0004557981
## 19 0.0004466196
## 20 0.0004453456
## 21 0.0004429048
## 22 0.0004518993
## 23 0.0004379285
## 24 0.0004465630
## 25 0.0004592782
## 26 0.0004735025
## 27 0.0004848565
## 28 0.0004793809
## 29 0.0004684547
## 30 0.0004624349
## 31 0.0004643623
## 32 0.0004527079
## 33 0.0004616697
## 34 0.0004666074
## 35 0.0004623012
## 36 0.0004550907
## 37 0.0004761892
## 38 0.0004813189
## 39 0.0004787012
## 40 0.0004864702
## 41 0.0004938228
## 42 0.0004864273
## 43 0.0005055377
## 44 0.0005171790
## 45 0.0005255145
## 46 0.0005269799
## 47 0.0005133467
## 48 0.0005206387
## 49 0.0005348896
## 50 0.0005518380
## 51 0.0005513937
## 52 0.0005319036
## 53 0.0005451481
## 54 0.0005399562
## 55 0.0005412661
## 56 0.0005417183
## 57 0.0005427385
## 58 0.0005482809
## 59 0.0005433028
## 60 0.0005533246
## 61 0.0005445195
## 62 0.0005663192
## 63 0.0005654507
## 64 0.0005721150
## 65 0.0005858309
## 66 0.0005973548
## 67 0.0005946034
## 68 0.0005975848
## 69 0.0006092531
## 70 0.0006100484
## 71 0.0006073626
## 72 0.0006003446
## 73 0.0005948896
## 74 0.0005956492
## 75 0.0005991201
## 76 0.0005982856
## 77 0.0005976154
## 78 0.0005907601
## 79 0.0005830541
## 80 0.0005777160
## 81 0.0005747003
## 82 0.0005809020
## 83 0.0005808642
## 84 0.0005815116
## 85 0.0005776121
## 86 0.0005666066
## 87 0.0005665141
## 88 0.0005726490
## 89 0.0005676991
## 90 0.0005676389
## 91 0.0005607866
## 92 0.0005606549
## 93 0.0005647758
## 94 0.0005588748
## 95 0.0005644994
## 96 0.0005638504
## 97 0.0005505650
## 98 0.0005497295
## 99 0.0005673417
## 100 0.0005657319
## 101 0.0005666750
## 102 0.0005716548
## 103 0.0005732765
## 104 0.0005667196
## 105 0.0005692025
## 106 0.0005764886
## 107 0.0005847642
## 108 0.0005865836
## 109 0.0005913864
## 110 0.0005963548
## 111 0.0005975086
## 112 0.0005876688
## 113 0.0005843044
## 114 0.0005842278
## 115 0.0005826612
## 116 0.0005854666
## 117 0.0005910795
## 118 0.0005902498
## 119 0.0005857763
## 120 0.0005844423
## 121 0.0005893846
## 122 0.0005913034
## 123 0.0005875533
## 124 0.0005896728
## 125 0.0005903994
## 126 0.0005879755
## 127 0.0005823860
## 128 0.0005829748
## 129 0.0005789386
## 130 0.0005813008
## 131 0.0005852766
## 132 0.0005799561
## 133 0.0005839250
## 134 0.0005860830
## 135 0.0005966914
## 136 0.0005931020
## 137 0.0005923046
## 138 0.0005923159
## 139 0.0005966040
## 140 0.0005974019
## 141 0.0005971932
## 142 0.0005907731
## 143 0.0005940754
## 144 0.0005958785
## 145 0.0005954800
## 146 0.0005924315
## 147 0.0005960955
## 148 0.0005934932
## 149 0.0005958900
## 150 0.0005932081
## 151 0.0005867798
## 152 0.0005877906
## 153 0.0005901562
## 154 0.0005924916
## 155 0.0005954319
## 156 0.0005881171
## 157 0.0005876005
## 158 0.0005890193
## 159 0.0005940532
## 160 0.0005912538
## 161 0.0005920677
## 162 0.0005853406
## 163 0.0005855101
## 164 0.0005871683
## 165 0.0005878264
## 166 0.0005876699
## 167 0.0005861051
## 168 0.0005854185
## 169 0.0005872321
## 170 0.0005846785
## 171 0.0005813525
## 172 0.0005836572
## 173 0.0005823769
## 174 0.0005827727
## 175 0.0005834631
## 176 0.0005810244
## 177 0.0005755621
## 178 0.0005745948
## 179 0.0005771867
## 180 0.0005760616
## 181 0.0005738939
## 182 0.0005760492
## 183 0.0005756568
## 184 0.0005760078
## 185 0.0005801453
## 186 0.0005811294
## 187 0.0005815796
## 188 0.0005794959
## 189 0.0005798678
## 190 0.0005785363
## 191 0.0005786231
## 192 0.0005778584
## 193 0.0005769357
## 194 0.0005766286
## 195 0.0005771973
## 196 0.0005792515
## 197 0.0005788342
## 198 0.0005800121
## 199 0.0005790795
## 200 0.0005807527
## 201 0.0005791041
## 202 0.0005783918
## 203 0.0005781571
## 204 0.0005812340
## 205 0.0005809667
## 206 0.0005828377
## 207 0.0005862895
## 208 0.0005848515
## 209 0.0005862627
## 210 0.0005877891
## 211 0.0005883912
## 212 0.0005876101
## 213 0.0005885022
## 214 0.0005867225
## 215 0.0005876808
## 216 0.0005872857
## 217 0.0005882544
## 218 0.0005899023
## 219 0.0005910193
## 220 0.0005901838
## 221 0.0005899732
## 222 0.0005903717
## 223 0.0005912456
## 224 0.0005909634
## 225 0.0005908327
## 226 0.0005909852
## 227 0.0005902674
## 228 0.0005909874
## 229 0.0005907499
## 230 0.0005903754
## 231 0.0005900148
## 232 0.0005902183
## 233 0.0005905081
## 234 0.0005910302
## 235 0.0005905733
## 236 0.0005909074
## 237 0.0005907941
## 238 0.0005905927
## 239 0.0005905728
## 240 0.0005905793
## nvmax
## 17 17
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Coefficients of final model:
## (Intercept) x4 x7 x8 x9
## 1.956954e+00 -5.190467e-05 1.236547e-02 5.936782e-04 3.236841e-03
## x10 x11 x16 x17 x21
## 1.456647e-03 2.465398e+05 8.171441e-04 1.482294e-03 1.229679e-04
## stat14 stat23 stat60 stat98 stat110
## -8.357780e-04 7.089590e-04 6.399162e-04 3.329090e-03 -3.310512e-03
## stat114 stat144 sqrt.x18
## 5.940544e-04 6.184670e-04 2.663657e-02Test
if (algo.backward.caret == TRUE){
test.model(model.backward, data.test
,method = 'leapBackward',subopt = NULL
,formula = formula, feature.names = feature.names, label.names = label.names
,id = id
,draw.limits = TRUE, transformation = t)
}## [1] "Summary of predicted values: "
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.033 2.080 2.093 2.093 2.106 2.149
## [1] "leapBackward Test MSE: 0.0010547435861647"
Stepwise Selection (w/ full train)
Train
if (algo.stepwise == TRUE){
t1 = Sys.time()
model.stepwise = step(model.null, scope=list(upper=model.full), data = data.train, direction="both", trace = 0)
print(summary(model.stepwise))
t2 = Sys.time()
print (paste("Time taken for Stepwise Selection: ",t2-t1, sep = ""))
plot.diagnostics(model.stepwise, data.train)
}Test
if (algo.stepwise == TRUE){
test.model(model.stepwise, data.test, "Stepwise Selection")
}Stepwise Selection (w/ filtered train)
Train
if (algo.stepwise == TRUE){
t1 = Sys.time()
model.stepwise2 = step(model.null2, scope=list(upper=model.full2), data = data.train2, direction="both", trace = 0)
print(summary(model.stepwise2))
t2 = Sys.time()
print (paste("Time taken for Stepwise Selection: ",t2-t1, sep = ""))
plot.diagnostics(model.stepwise2, data.train2)
}Test
if (algo.stepwise == TRUE){
test.model(model.stepwise2, data.test, "Stepwise Selection (2)")
}Stepwise Selection with CV (w/ full train)
Train
if (algo.stepwise.caret == TRUE){
set.seed(1)
returned = train.caret.glmselect(formula = formula
,data = data.train
,method = "leapSeq"
,feature.names = feature.names)
model.stepwise = returned$model
id = returned$id
}## Aggregating results
## Selecting tuning parameters
## Fitting nvmax = 7 on full training set
## nvmax RMSE Rsquared MAE RMSESD RsquaredSD
## 1 1 0.03410610 0.1149630 0.02657577 0.0012861157 0.02115810
## 2 2 0.03328304 0.1574261 0.02582705 0.0011082962 0.02564652
## 3 3 0.03267992 0.1875344 0.02522129 0.0010042512 0.02555459
## 4 4 0.03218662 0.2115343 0.02451101 0.0010005851 0.02634272
## 5 5 0.03186632 0.2271994 0.02432100 0.0009679660 0.02729495
## 6 6 0.03185480 0.2277527 0.02432260 0.0009428484 0.02753486
## 7 7 0.03174204 0.2329557 0.02426652 0.0009365345 0.02626930
## 8 8 0.03177661 0.2313360 0.02430354 0.0009495410 0.02509086
## 9 9 0.03177342 0.2315882 0.02429654 0.0009401013 0.02649936
## 10 10 0.03174421 0.2330436 0.02428987 0.0009336330 0.02724717
## 11 11 0.03175240 0.2327719 0.02430959 0.0009426783 0.02703386
## 12 12 0.03174790 0.2330175 0.02429329 0.0009361445 0.02713821
## 13 13 0.03176566 0.2322273 0.02431153 0.0009301109 0.02725054
## 14 14 0.03174821 0.2331087 0.02430265 0.0008876305 0.02909070
## 15 15 0.03175781 0.2326158 0.02430769 0.0009302689 0.02811104
## 16 16 0.03211926 0.2149269 0.02460232 0.0016589574 0.05640281
## 17 17 0.03178911 0.2311487 0.02432604 0.0009658569 0.02635184
## 18 18 0.03177643 0.2317125 0.02431717 0.0009793686 0.02490692
## 19 19 0.03175964 0.2325262 0.02429739 0.0009456329 0.02627921
## 20 20 0.03175338 0.2328159 0.02429680 0.0009786020 0.02653806
## 21 21 0.03174981 0.2329992 0.02429780 0.0009964310 0.02533156
## 22 22 0.03176674 0.2322271 0.02429902 0.0009904555 0.02511125
## 23 23 0.03175392 0.2328407 0.02429455 0.0009774665 0.02551565
## 24 24 0.03282883 0.1803104 0.02519597 0.0023155572 0.08101563
## 25 25 0.03209541 0.2150686 0.02458775 0.0011601513 0.06094698
## 26 26 0.03241861 0.1992981 0.02482324 0.0018465750 0.07735846
## 27 27 0.03215188 0.2127444 0.02458993 0.0011141649 0.05511356
## 28 28 0.03180694 0.2305555 0.02432526 0.0009425082 0.02679435
## 29 29 0.03181979 0.2300975 0.02434237 0.0009522165 0.02721588
## 30 30 0.03211578 0.2142500 0.02463198 0.0011747173 0.05962565
## 31 31 0.03237591 0.2000485 0.02485706 0.0013293160 0.08169943
## 32 32 0.03233612 0.2022149 0.02474432 0.0014210357 0.08154324
## 33 33 0.03183478 0.2294410 0.02434773 0.0009821203 0.02653906
## 34 34 0.03184403 0.2290906 0.02437048 0.0009826630 0.02640569
## 35 35 0.03250160 0.1957711 0.02494104 0.0016536733 0.07164746
## 36 36 0.03221350 0.2122502 0.02465580 0.0019058140 0.04598983
## 37 37 0.03190358 0.2264476 0.02440120 0.0009828211 0.02468246
## 38 38 0.03189749 0.2267378 0.02440712 0.0009965136 0.02346400
## 39 39 0.03212960 0.2141372 0.02456237 0.0012831713 0.05657506
## 40 40 0.03188959 0.2271720 0.02441256 0.0009994361 0.02341634
## 41 41 0.03216293 0.2123877 0.02463264 0.0012073624 0.06311081
## 42 42 0.03248341 0.1980727 0.02484670 0.0020466349 0.06508109
## 43 43 0.03193239 0.2253818 0.02444486 0.0010081867 0.02329288
## 44 44 0.03229425 0.2067995 0.02473590 0.0011282630 0.05080137
## 45 45 0.03226414 0.2091858 0.02474148 0.0016519816 0.05380955
## 46 46 0.03193405 0.2254265 0.02445603 0.0009882143 0.02454566
## 47 47 0.03193352 0.2254485 0.02445708 0.0009984702 0.02437626
## 48 48 0.03256762 0.1943422 0.02495475 0.0019731802 0.07120842
## 49 49 0.03223562 0.2100239 0.02472631 0.0012259703 0.05111596
## 50 50 0.03195650 0.2245477 0.02447378 0.0009856750 0.02497266
## 51 51 0.03196159 0.2243297 0.02448205 0.0009940737 0.02482403
## 52 52 0.03196892 0.2240017 0.02447639 0.0010003420 0.02481363
## 53 53 0.03257242 0.1935117 0.02503046 0.0017588808 0.06676153
## 54 54 0.03259778 0.1910291 0.02497816 0.0012915290 0.07599770
## 55 55 0.03225370 0.2087661 0.02473797 0.0010950232 0.05601349
## 56 56 0.03229253 0.2091512 0.02472056 0.0019238341 0.04665426
## 57 57 0.03259075 0.1934163 0.02499611 0.0018924363 0.06449884
## 58 58 0.03196489 0.2243476 0.02447793 0.0010191719 0.02536728
## 59 59 0.03227592 0.2079756 0.02475775 0.0010783505 0.05623206
## 60 60 0.03229540 0.2074869 0.02478128 0.0012692020 0.05501859
## 61 61 0.03199268 0.2231779 0.02449705 0.0009959766 0.02529087
## 62 62 0.03219441 0.2117503 0.02464888 0.0012774142 0.05750873
## 63 63 0.03298815 0.1739322 0.02532744 0.0022636622 0.07631895
## 64 64 0.03289132 0.1768382 0.02525706 0.0013835740 0.07592171
## 65 65 0.03252690 0.1952872 0.02492907 0.0014500114 0.07353741
## 66 66 0.03283150 0.1792645 0.02521179 0.0018147869 0.08321847
## 67 67 0.03200293 0.2228634 0.02450889 0.0009897985 0.02631332
## 68 68 0.03333049 0.1555654 0.02560361 0.0019689929 0.08672999
## 69 69 0.03316138 0.1639111 0.02544118 0.0020431124 0.08380379
## 70 70 0.03274195 0.1868649 0.02514319 0.0020458536 0.07073018
## 71 71 0.03254776 0.1934834 0.02497525 0.0013035213 0.07876418
## 72 72 0.03259936 0.1912174 0.02504414 0.0011373458 0.07200664
## 73 73 0.03232360 0.2071354 0.02473704 0.0013259988 0.04911588
## 74 74 0.03231690 0.2064387 0.02478512 0.0011828164 0.05812581
## 75 75 0.03323928 0.1616505 0.02546019 0.0023840683 0.07914280
## 76 76 0.03237452 0.2044338 0.02481586 0.0012769940 0.05676240
## 77 77 0.03313903 0.1678332 0.02538913 0.0022451230 0.07294021
## 78 78 0.03238459 0.2047211 0.02484257 0.0016077549 0.05345208
## 79 79 0.03258366 0.1931998 0.02496386 0.0014389802 0.07300195
## 80 80 0.03207236 0.2201539 0.02455813 0.0009834858 0.02803806
## 81 81 0.03235293 0.2059608 0.02476558 0.0013093083 0.04885907
## 82 82 0.03239624 0.2035417 0.02482618 0.0012849801 0.05715164
## 83 83 0.03245366 0.2014963 0.02485597 0.0016493830 0.05805375
## 84 84 0.03239949 0.2041724 0.02485379 0.0015792852 0.05188819
## 85 85 0.03228875 0.2080722 0.02468667 0.0012639879 0.05703009
## 86 86 0.03237868 0.2039564 0.02482511 0.0010080674 0.05412163
## 87 87 0.03210356 0.2188564 0.02457060 0.0009851321 0.02785877
## 88 88 0.03246338 0.2020755 0.02483721 0.0019546927 0.05074489
## 89 89 0.03211609 0.2182976 0.02458036 0.0009808036 0.02763042
## 90 90 0.03210117 0.2190102 0.02456896 0.0009863785 0.02825073
## 91 91 0.03304188 0.1724487 0.02531407 0.0021383158 0.07134435
## 92 92 0.03246262 0.2005750 0.02484400 0.0011241658 0.05378501
## 93 93 0.03257410 0.1928503 0.02495444 0.0013866349 0.07476945
## 94 94 0.03247654 0.2007705 0.02487414 0.0016558227 0.05873577
## 95 95 0.03210040 0.2192415 0.02456970 0.0009769006 0.02855142
## 96 96 0.03276104 0.1864404 0.02512267 0.0019211575 0.06573290
## 97 97 0.03274925 0.1877231 0.02506480 0.0021247668 0.06233909
## 98 98 0.03240481 0.2034406 0.02484317 0.0012521020 0.05650603
## 99 99 0.03211732 0.2185770 0.02458936 0.0009655235 0.02837108
## 100 100 0.03279035 0.1847521 0.02516988 0.0015841894 0.06490836
## 101 101 0.03276582 0.1858219 0.02511647 0.0017499201 0.07979457
## 102 102 0.03233659 0.2064519 0.02472461 0.0012848699 0.05944033
## 103 103 0.03242725 0.2022871 0.02486848 0.0009823160 0.05536879
## 104 104 0.03274622 0.1870312 0.02517483 0.0016946199 0.06783572
## 105 105 0.03232916 0.2068404 0.02472680 0.0012845203 0.05932755
## 106 106 0.03304363 0.1703387 0.02540484 0.0016136926 0.08098415
## 107 107 0.03239277 0.2047053 0.02479968 0.0013082155 0.04906237
## 108 108 0.03276245 0.1866209 0.02510418 0.0018275654 0.06796938
## 109 109 0.03244999 0.2014355 0.02488959 0.0010011122 0.05543930
## 110 110 0.03214482 0.2176040 0.02460990 0.0009870561 0.02818638
## 111 111 0.03272864 0.1875685 0.02504358 0.0018351050 0.07649956
## 112 112 0.03269290 0.1891129 0.02506228 0.0014507887 0.07353850
## 113 113 0.03297536 0.1736356 0.02527700 0.0018556467 0.08556914
## 114 114 0.03241320 0.2034111 0.02484082 0.0012132860 0.06425824
## 115 115 0.03305682 0.1704426 0.02539493 0.0013465786 0.07471932
## 116 116 0.03251320 0.1996044 0.02490583 0.0016703408 0.05915495
## 117 117 0.03312782 0.1698228 0.02543830 0.0023680359 0.06921822
## 118 118 0.03242369 0.2029767 0.02484708 0.0012025350 0.06384521
## 119 119 0.03243619 0.2028915 0.02484124 0.0013332787 0.05026899
## 120 120 0.03242022 0.2043535 0.02485374 0.0014404317 0.04469422
## 121 121 0.03252591 0.1968981 0.02494054 0.0012469272 0.05854280
## 122 122 0.03275149 0.1869262 0.02512400 0.0009048483 0.05692935
## 123 123 0.03275282 0.1878138 0.02512222 0.0014183162 0.05465716
## 124 124 0.03254242 0.1961844 0.02495684 0.0012560789 0.05897256
## 125 125 0.03263963 0.1932637 0.02505210 0.0015572354 0.05175062
## 126 126 0.03273151 0.1893005 0.02510157 0.0017938955 0.05807539
## 127 127 0.03233508 0.2071176 0.02473006 0.0012017460 0.05157564
## 128 128 0.03243324 0.2029735 0.02486546 0.0009228015 0.04664238
## 129 129 0.03273711 0.1880768 0.02509641 0.0011570328 0.06640368
## 130 130 0.03233926 0.2070006 0.02474230 0.0011992653 0.05141405
## 131 131 0.03288517 0.1798324 0.02526493 0.0011435016 0.06870453
## 132 132 0.03234856 0.2080943 0.02481654 0.0012697575 0.03542431
## 133 133 0.03278756 0.1856771 0.02511570 0.0016008352 0.05637292
## 134 134 0.03218274 0.2162132 0.02464776 0.0010068218 0.02777427
## 135 135 0.03279191 0.1848163 0.02512615 0.0010797685 0.05653861
## 136 136 0.03239629 0.2052970 0.02480013 0.0008914681 0.04033008
## 137 137 0.03252216 0.1988631 0.02493976 0.0013069565 0.05249076
## 138 138 0.03217889 0.2164334 0.02464307 0.0010136988 0.02738787
## 139 139 0.03217696 0.2164877 0.02463722 0.0010174389 0.02705326
## 140 140 0.03267881 0.1893920 0.02504305 0.0013274950 0.06095029
## 141 141 0.03218443 0.2161928 0.02463989 0.0010208299 0.02693775
## 142 142 0.03260225 0.1958824 0.02507148 0.0016185036 0.04216231
## 143 143 0.03264340 0.1930923 0.02501013 0.0010986146 0.04339204
## 144 144 0.03220393 0.2153952 0.02465968 0.0010136477 0.02700242
## 145 145 0.03237407 0.2062210 0.02476093 0.0012205137 0.05012499
## 146 146 0.03264253 0.1928145 0.02499230 0.0010059415 0.05427841
## 147 147 0.03246579 0.2021898 0.02486344 0.0009881039 0.03905779
## 148 148 0.03239417 0.2058036 0.02480383 0.0011742174 0.03727277
## 149 149 0.03278908 0.1867535 0.02515068 0.0018708417 0.05568879
## 150 150 0.03236611 0.2062796 0.02481525 0.0010909673 0.04337332
## 151 151 0.03286938 0.1814134 0.02522398 0.0007685191 0.04808198
## 152 152 0.03240807 0.2051952 0.02481308 0.0011819189 0.03726666
## 153 153 0.03222093 0.2146720 0.02465821 0.0010361823 0.02691990
## 154 154 0.03284688 0.1831146 0.02518447 0.0012298273 0.05565941
## 155 155 0.03222568 0.2144840 0.02466137 0.0010258089 0.02704617
## 156 156 0.03346262 0.1517340 0.02574161 0.0013816858 0.05763817
## 157 157 0.03223805 0.2139676 0.02466573 0.0010311105 0.02721483
## 158 158 0.03249287 0.2017395 0.02489567 0.0014418380 0.04438291
## 159 159 0.03242227 0.2043929 0.02479954 0.0011134152 0.05030422
## 160 160 0.03267059 0.1923989 0.02500186 0.0016097881 0.06097249
## 161 161 0.03272187 0.1912690 0.02510586 0.0018867339 0.04788721
## 162 162 0.03222553 0.2145163 0.02465750 0.0010336107 0.02704354
## 163 163 0.03307779 0.1716100 0.02535995 0.0016500439 0.06171150
## 164 164 0.03221908 0.2147781 0.02465538 0.0010323753 0.02687193
## 165 165 0.03266885 0.1925422 0.02502351 0.0014827960 0.05985727
## 166 166 0.03239995 0.2053253 0.02485827 0.0010900803 0.03932986
## 167 167 0.03260356 0.1951139 0.02491845 0.0013365792 0.06649378
## 168 168 0.03257180 0.1964623 0.02502186 0.0013491707 0.04867899
## 169 169 0.03248084 0.2023802 0.02489528 0.0014612445 0.04515946
## 170 170 0.03259162 0.1960716 0.02499438 0.0013587709 0.05423047
## 171 171 0.03222250 0.2147113 0.02466286 0.0010283200 0.02705396
## 172 172 0.03222393 0.2146528 0.02466829 0.0010252256 0.02699643
## 173 173 0.03245234 0.2041939 0.02487745 0.0016097430 0.03542971
## 174 174 0.03298623 0.1772855 0.02528479 0.0012388986 0.05321625
## 175 175 0.03222688 0.2145505 0.02466627 0.0010305452 0.02698883
## 176 176 0.03223017 0.2144153 0.02467052 0.0010336480 0.02679063
## 177 177 0.03249353 0.2012871 0.02488427 0.0009989004 0.03806167
## 178 178 0.03250905 0.2012687 0.02492783 0.0014939364 0.04621292
## 179 179 0.03242742 0.2044618 0.02483726 0.0011979018 0.03784734
## 180 180 0.03260993 0.1955626 0.02502549 0.0014219431 0.04298846
## 181 181 0.03270422 0.1913350 0.02507046 0.0015211676 0.06083643
## 182 182 0.03242496 0.2044994 0.02481382 0.0011148913 0.05048274
## 183 183 0.03244749 0.2034140 0.02482378 0.0009295843 0.04079385
## 184 184 0.03242398 0.2045284 0.02481707 0.0011187735 0.05053383
## 185 185 0.03222617 0.2145783 0.02467325 0.0010385848 0.02728563
## 186 186 0.03270729 0.1913902 0.02505777 0.0016844958 0.06387269
## 187 187 0.03289159 0.1822055 0.02521764 0.0016019211 0.04768519
## 188 188 0.03271595 0.1910402 0.02506996 0.0016916118 0.06414302
## 189 189 0.03222725 0.2145114 0.02467832 0.0010422847 0.02720718
## 190 190 0.03222934 0.2144297 0.02468033 0.0010440887 0.02729145
## 191 191 0.03222885 0.2144459 0.02467938 0.0010426642 0.02733943
## 192 192 0.03222873 0.2144454 0.02467910 0.0010437582 0.02739262
## 193 193 0.03223054 0.2143701 0.02468088 0.0010438126 0.02745762
## 194 194 0.03269836 0.1912423 0.02502538 0.0012588830 0.05785350
## 195 195 0.03243547 0.2041273 0.02484590 0.0012201843 0.03905888
## 196 196 0.03252376 0.2007439 0.02494787 0.0015375568 0.04803153
## 197 197 0.03271104 0.1903672 0.02504747 0.0008196595 0.04539275
## 198 198 0.03241980 0.2045562 0.02489094 0.0011153328 0.04001522
## 199 199 0.03244998 0.2033016 0.02483737 0.0009445484 0.04134277
## 200 200 0.03223011 0.2143799 0.02468214 0.0010477634 0.02755269
## 201 201 0.03241837 0.2046066 0.02488992 0.0011188097 0.04009894
## 202 202 0.03223170 0.2143143 0.02468308 0.0010448119 0.02759767
## 203 203 0.03249471 0.2012975 0.02489545 0.0010046360 0.03826399
## 204 204 0.03223128 0.2143422 0.02468494 0.0010437813 0.02748991
## 205 205 0.03223160 0.2143291 0.02468513 0.0010444590 0.02757676
## 206 206 0.03223155 0.2143227 0.02468628 0.0010464766 0.02756180
## 207 207 0.03245677 0.2030212 0.02484584 0.0009477787 0.04166573
## 208 208 0.03244545 0.2040013 0.02482601 0.0013569545 0.05499953
## 209 209 0.03223383 0.2142319 0.02468739 0.0010453155 0.02759553
## 210 210 0.03223320 0.2142497 0.02468694 0.0010465787 0.02753182
## 211 211 0.03269911 0.1925198 0.02506673 0.0018671964 0.05860282
## 212 212 0.03265499 0.1930796 0.02500867 0.0010830323 0.04642058
## 213 213 0.03223343 0.2142513 0.02468865 0.0010461559 0.02751035
## 214 214 0.03245071 0.2039037 0.02483228 0.0013620020 0.05495304
## 215 215 0.03223403 0.2142238 0.02468962 0.0010456569 0.02733998
## 216 216 0.03223412 0.2142200 0.02469021 0.0010437127 0.02725333
## 217 217 0.03253702 0.2002844 0.02495545 0.0015561583 0.04835077
## 218 218 0.03271720 0.1918776 0.02508091 0.0018928658 0.05962750
## 219 219 0.03243091 0.2040993 0.02491197 0.0011267901 0.04066818
## 220 220 0.03242743 0.2040563 0.02488889 0.0011401401 0.04617853
## 221 221 0.03264785 0.1939217 0.02497485 0.0012311441 0.05891513
## 222 222 0.03223526 0.2141694 0.02469290 0.0010456283 0.02720763
## 223 223 0.03243332 0.2040111 0.02491421 0.0011295493 0.04078651
## 224 224 0.03245215 0.2036891 0.02491210 0.0014086216 0.04099450
## 225 225 0.03223502 0.2141819 0.02469298 0.0010445208 0.02725529
## 226 226 0.03243786 0.2042074 0.02484177 0.0011371523 0.05092127
## 227 227 0.03223413 0.2142199 0.02469274 0.0010449003 0.02723661
## 228 228 0.03249782 0.2022641 0.02493262 0.0017245235 0.03937861
## 229 229 0.03265988 0.1937400 0.02501446 0.0014782201 0.05951571
## 230 230 0.03250115 0.2012147 0.02490329 0.0010145338 0.03807589
## 231 231 0.03294674 0.1798964 0.02532156 0.0016182348 0.05546818
## 232 232 0.03244959 0.2032430 0.02490565 0.0011714890 0.04824992
## 233 233 0.03285569 0.1833735 0.02530027 0.0015113833 0.05125967
## 234 234 0.03223392 0.2142269 0.02469274 0.0010457886 0.02725864
## 235 235 0.03244302 0.2037073 0.02484680 0.0009360417 0.03961460
## 236 236 0.03317742 0.1682341 0.02554772 0.0014218553 0.05160845
## 237 237 0.03313482 0.1698927 0.02547479 0.0014243469 0.05975247
## 238 238 0.03324611 0.1660447 0.02562257 0.0018436969 0.05124826
## 239 239 0.03312973 0.1708756 0.02553320 0.0017869280 0.05201908
## 240 240 0.03223413 0.2142186 0.02469321 0.0010461107 0.02727501
## MAESD
## 1 0.0006039987
## 2 0.0005560515
## 3 0.0005161705
## 4 0.0005030416
## 5 0.0004516171
## 6 0.0004274403
## 7 0.0004386333
## 8 0.0004116784
## 9 0.0004027648
## 10 0.0003804190
## 11 0.0003827175
## 12 0.0003714371
## 13 0.0003718175
## 14 0.0003455862
## 15 0.0003658994
## 16 0.0011127009
## 17 0.0003957368
## 18 0.0004141335
## 19 0.0003935137
## 20 0.0004296221
## 21 0.0004388117
## 22 0.0004236905
## 23 0.0004130209
## 24 0.0015775448
## 25 0.0008449812
## 26 0.0013036300
## 27 0.0007547808
## 28 0.0004085353
## 29 0.0004149682
## 30 0.0008792404
## 31 0.0011119828
## 32 0.0010121724
## 33 0.0004354754
## 34 0.0004395554
## 35 0.0012482211
## 36 0.0010576188
## 37 0.0004392410
## 38 0.0004605232
## 39 0.0007178040
## 40 0.0004695298
## 41 0.0008813114
## 42 0.0011705339
## 43 0.0004917127
## 44 0.0007111468
## 45 0.0011818184
## 46 0.0004753556
## 47 0.0004734905
## 48 0.0012402559
## 49 0.0008601080
## 50 0.0004511619
## 51 0.0004427788
## 52 0.0004748062
## 53 0.0013011698
## 54 0.0010152414
## 55 0.0007785009
## 56 0.0010627563
## 57 0.0011516998
## 58 0.0004619953
## 59 0.0007652451
## 60 0.0008966005
## 61 0.0004482238
## 62 0.0007188133
## 63 0.0014967725
## 64 0.0011747955
## 65 0.0011522404
## 66 0.0013298873
## 67 0.0004425742
## 68 0.0015170473
## 69 0.0014324750
## 70 0.0014989525
## 71 0.0010586854
## 72 0.0009230817
## 73 0.0009113305
## 74 0.0008430655
## 75 0.0015875591
## 76 0.0009212945
## 77 0.0014489533
## 78 0.0011305868
## 79 0.0011414438
## 80 0.0004694116
## 81 0.0009021172
## 82 0.0009181138
## 83 0.0011499452
## 84 0.0011156066
## 85 0.0007122391
## 86 0.0007046124
## 87 0.0004611376
## 88 0.0010679946
## 89 0.0004528057
## 90 0.0004514514
## 91 0.0013852193
## 92 0.0007365208
## 93 0.0009726072
## 94 0.0011484765
## 95 0.0004588348
## 96 0.0011437201
## 97 0.0013033810
## 98 0.0009003507
## 99 0.0004546460
## 100 0.0011675947
## 101 0.0013553714
## 102 0.0007464478
## 103 0.0007337989
## 104 0.0012938032
## 105 0.0007421953
## 106 0.0012975227
## 107 0.0008976272
## 108 0.0013508162
## 109 0.0007339981
## 110 0.0004534559
## 111 0.0012652426
## 112 0.0011603051
## 113 0.0013499275
## 114 0.0009012387
## 115 0.0011555386
## 116 0.0011608498
## 117 0.0015535712
## 118 0.0008889101
## 119 0.0009281055
## 120 0.0009074940
## 121 0.0008387336
## 122 0.0007123429
## 123 0.0009610969
## 124 0.0008405230
## 125 0.0011075471
## 126 0.0012151338
## 127 0.0006195299
## 128 0.0005963730
## 129 0.0008949365
## 130 0.0006240965
## 131 0.0010349946
## 132 0.0007569814
## 133 0.0009219061
## 134 0.0004541498
## 135 0.0008053882
## 136 0.0004316646
## 137 0.0008587860
## 138 0.0004578164
## 139 0.0004576245
## 140 0.0009058995
## 141 0.0004591899
## 142 0.0010163552
## 143 0.0007295024
## 144 0.0004475773
## 145 0.0006326773
## 146 0.0006655580
## 147 0.0005395318
## 148 0.0007327373
## 149 0.0011238014
## 150 0.0006657392
## 151 0.0005760039
## 152 0.0007357123
## 153 0.0004620058
## 154 0.0008663381
## 155 0.0004548887
## 156 0.0010976095
## 157 0.0004515599
## 158 0.0009635948
## 159 0.0006603004
## 160 0.0010652083
## 161 0.0012139585
## 162 0.0004519747
## 163 0.0010039189
## 164 0.0004484753
## 165 0.0010671888
## 166 0.0007069726
## 167 0.0008303874
## 168 0.0009357179
## 169 0.0009983007
## 170 0.0009343876
## 171 0.0004493639
## 172 0.0004508349
## 173 0.0008982117
## 174 0.0009285355
## 175 0.0004531830
## 176 0.0004528845
## 177 0.0005359231
## 178 0.0010375023
## 179 0.0007517797
## 180 0.0009799448
## 181 0.0011209038
## 182 0.0006747597
## 183 0.0004342894
## 184 0.0006782662
## 185 0.0004668034
## 186 0.0011673016
## 187 0.0010038496
## 188 0.0011750050
## 189 0.0004674643
## 190 0.0004709530
## 191 0.0004718856
## 192 0.0004708967
## 193 0.0004726529
## 194 0.0007447920
## 195 0.0007688918
## 196 0.0010879613
## 197 0.0004385228
## 198 0.0007285305
## 199 0.0004526595
## 200 0.0004763508
## 201 0.0007329246
## 202 0.0004731103
## 203 0.0005422353
## 204 0.0004715263
## 205 0.0004730446
## 206 0.0004730972
## 207 0.0004550047
## 208 0.0007670386
## 209 0.0004727704
## 210 0.0004721833
## 211 0.0011141488
## 212 0.0007042798
## 213 0.0004703265
## 214 0.0007714124
## 215 0.0004682378
## 216 0.0004668158
## 217 0.0010797776
## 218 0.0011290609
## 219 0.0007583249
## 220 0.0007719019
## 221 0.0007138473
## 222 0.0004665774
## 223 0.0007577915
## 224 0.0009035060
## 225 0.0004664618
## 226 0.0006960714
## 227 0.0004670497
## 228 0.0009809959
## 229 0.0009751296
## 230 0.0005431403
## 231 0.0011912097
## 232 0.0008067244
## 233 0.0011281128
## 234 0.0004674003
## 235 0.0004394913
## 236 0.0011343020
## 237 0.0011578774
## 238 0.0012441954
## 239 0.0011540559
## 240 0.0004675061
## nvmax
## 7 7
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Coefficients of final model:
## (Intercept) x4 x7 x9 x17
## 2.006915e+00 -4.541413e-05 1.108905e-02 3.535570e-03 1.516178e-03
## stat98 stat110 sqrt.x18
## 3.579154e-03 -3.275780e-03 2.665541e-02Test
if (algo.stepwise.caret == TRUE){
test.model(model.stepwise, data.test
,method = 'leapSeq',subopt = NULL
,formula = formula, feature.names = feature.names, label.names = label.names
,id = id
,draw.limits = TRUE, transformation = t)
}## [1] "Summary of predicted values: "
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.041 2.084 2.097 2.096 2.109 2.142
## [1] "leapSeq Test MSE: 0.00103851731581386"
Stepwise Selection with CV (w/ filtered train)
Train
Test
LASSO (w/ full train)
Train
if(algo.LASSO == TRUE){
# Formatting data for GLM net
# you can use model.matrix as well -- model.matrix creates a design (or model) matrix,
# e.g., by expanding factors to a set of dummy variables (depending on the contrasts)
# and expanding interactions similarly.
x = as.matrix(data.train[,feature.names])
y = data.train[,label.names]
xtest = as.matrix(data.test[,feature.names])
ytest = data.test[,label.names]
grid=10^seq(10,-2, length =100)
set.seed(1)
model.LASSO=glmnet(x,y,alpha=1, lambda =grid)
cv.out=cv.glmnet(x,y,alpha=1) # alpha=1 performs LASSO
plot(cv.out)
bestlambda<-cv.out$lambda.min # Optimal penalty parameter. You can make this call visually.
print(coef(model.LASSO,s=bestlambda))
}Test
if(algo.LASSO == TRUE){
lasso.pred=predict (model.LASSO ,s=bestlambda ,newx=xtest)
testMSE_LASSO = mean((ytest-lasso.pred)^2)
print (paste("LASSO Test RMSE: ",testMSE_LASSO, sep=""))
plot(ytest,lasso.pred)
}LASSO (w/ filtered train)
Train
if(algo.LASSO == TRUE){
# Formatting data for GLM net
# you can use model.matrix as well -- model.matrix creates a design (or model) matrix,
# e.g., by expanding factors to a set of dummy variables (depending on the contrasts)
# and expanding interactions similarly.
x = as.matrix(data.train2[,feature.names])
y = data.train2[,label.names]
xtest = as.matrix(data.test[,feature.names])
ytest = data.test[,label.names]
grid=10^seq(10,-2, length =100)
set.seed(1)
model.LASSO=glmnet(x,y,alpha=1, lambda =grid)
cv.out=cv.glmnet(x,y,alpha=1) # alpha=1 performs LASSO
plot(cv.out)
bestlambda<-cv.out$lambda.min # Optimal penalty parameter. You can make this call visually.
print(coef(model.LASSO,s=bestlambda))
}Test
if(algo.LASSO == TRUE){
lasso.pred=predict (model.LASSO ,s=bestlambda ,newx=xtest)
testMSE_LASSO = mean((ytest-lasso.pred)^2)
print (paste("LASSO Test RMSE: ",testMSE_LASSO, sep=""))
plot(ytest,lasso.pred)
}LASSO with CV (w/ full train)
Train
if (algo.LASSO.caret == TRUE){
set.seed(1)
returned = train.caret.glmselect(formula = formula
,data = data.train
,method = "glmnet"
,subopt = 'LASSO'
,feature.names = feature.names)
model.LASSO.caret = returned$model
}## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.01 on full training set
## glmnet
##
## 6002 samples
## 240 predictor
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 5402, 5401, 5402, 5401, 5402, 5402, ...
## Resampling results across tuning parameters:
##
## lambda RMSE Rsquared MAE
## 0.01000000 0.03553800 0.114963 0.02750353
## 0.01047616 0.03567477 0.114963 0.02759932
## 0.01097499 0.03582429 0.114963 0.02770616
## 0.01149757 0.03598768 0.114963 0.02782456
## 0.01204504 0.03616167 0.109576 0.02795007
## 0.01261857 0.03623036 NaN 0.02799885
## 0.01321941 0.03623036 NaN 0.02799885
## 0.01384886 0.03623036 NaN 0.02799885
## 0.01450829 0.03623036 NaN 0.02799885
## 0.01519911 0.03623036 NaN 0.02799885
## 0.01592283 0.03623036 NaN 0.02799885
## 0.01668101 0.03623036 NaN 0.02799885
## 0.01747528 0.03623036 NaN 0.02799885
## 0.01830738 0.03623036 NaN 0.02799885
## 0.01917910 0.03623036 NaN 0.02799885
## 0.02009233 0.03623036 NaN 0.02799885
## 0.02104904 0.03623036 NaN 0.02799885
## 0.02205131 0.03623036 NaN 0.02799885
## 0.02310130 0.03623036 NaN 0.02799885
## 0.02420128 0.03623036 NaN 0.02799885
## 0.02535364 0.03623036 NaN 0.02799885
## 0.02656088 0.03623036 NaN 0.02799885
## 0.02782559 0.03623036 NaN 0.02799885
## 0.02915053 0.03623036 NaN 0.02799885
## 0.03053856 0.03623036 NaN 0.02799885
## 0.03199267 0.03623036 NaN 0.02799885
## 0.03351603 0.03623036 NaN 0.02799885
## 0.03511192 0.03623036 NaN 0.02799885
## 0.03678380 0.03623036 NaN 0.02799885
## 0.03853529 0.03623036 NaN 0.02799885
## 0.04037017 0.03623036 NaN 0.02799885
## 0.04229243 0.03623036 NaN 0.02799885
## 0.04430621 0.03623036 NaN 0.02799885
## 0.04641589 0.03623036 NaN 0.02799885
## 0.04862602 0.03623036 NaN 0.02799885
## 0.05094138 0.03623036 NaN 0.02799885
## 0.05336699 0.03623036 NaN 0.02799885
## 0.05590810 0.03623036 NaN 0.02799885
## 0.05857021 0.03623036 NaN 0.02799885
## 0.06135907 0.03623036 NaN 0.02799885
## 0.06428073 0.03623036 NaN 0.02799885
## 0.06734151 0.03623036 NaN 0.02799885
## 0.07054802 0.03623036 NaN 0.02799885
## 0.07390722 0.03623036 NaN 0.02799885
## 0.07742637 0.03623036 NaN 0.02799885
## 0.08111308 0.03623036 NaN 0.02799885
## 0.08497534 0.03623036 NaN 0.02799885
## 0.08902151 0.03623036 NaN 0.02799885
## 0.09326033 0.03623036 NaN 0.02799885
## 0.09770100 0.03623036 NaN 0.02799885
## 0.10235310 0.03623036 NaN 0.02799885
## 0.10722672 0.03623036 NaN 0.02799885
## 0.11233240 0.03623036 NaN 0.02799885
## 0.11768120 0.03623036 NaN 0.02799885
## 0.12328467 0.03623036 NaN 0.02799885
## 0.12915497 0.03623036 NaN 0.02799885
## 0.13530478 0.03623036 NaN 0.02799885
## 0.14174742 0.03623036 NaN 0.02799885
## 0.14849683 0.03623036 NaN 0.02799885
## 0.15556761 0.03623036 NaN 0.02799885
## 0.16297508 0.03623036 NaN 0.02799885
## 0.17073526 0.03623036 NaN 0.02799885
## 0.17886495 0.03623036 NaN 0.02799885
## 0.18738174 0.03623036 NaN 0.02799885
## 0.19630407 0.03623036 NaN 0.02799885
## 0.20565123 0.03623036 NaN 0.02799885
## 0.21544347 0.03623036 NaN 0.02799885
## 0.22570197 0.03623036 NaN 0.02799885
## 0.23644894 0.03623036 NaN 0.02799885
## 0.24770764 0.03623036 NaN 0.02799885
## 0.25950242 0.03623036 NaN 0.02799885
## 0.27185882 0.03623036 NaN 0.02799885
## 0.28480359 0.03623036 NaN 0.02799885
## 0.29836472 0.03623036 NaN 0.02799885
## 0.31257158 0.03623036 NaN 0.02799885
## 0.32745492 0.03623036 NaN 0.02799885
## 0.34304693 0.03623036 NaN 0.02799885
## 0.35938137 0.03623036 NaN 0.02799885
## 0.37649358 0.03623036 NaN 0.02799885
## 0.39442061 0.03623036 NaN 0.02799885
## 0.41320124 0.03623036 NaN 0.02799885
## 0.43287613 0.03623036 NaN 0.02799885
## 0.45348785 0.03623036 NaN 0.02799885
## 0.47508102 0.03623036 NaN 0.02799885
## 0.49770236 0.03623036 NaN 0.02799885
## 0.52140083 0.03623036 NaN 0.02799885
## 0.54622772 0.03623036 NaN 0.02799885
## 0.57223677 0.03623036 NaN 0.02799885
## 0.59948425 0.03623036 NaN 0.02799885
## 0.62802914 0.03623036 NaN 0.02799885
## 0.65793322 0.03623036 NaN 0.02799885
## 0.68926121 0.03623036 NaN 0.02799885
## 0.72208090 0.03623036 NaN 0.02799885
## 0.75646333 0.03623036 NaN 0.02799885
## 0.79248290 0.03623036 NaN 0.02799885
## 0.83021757 0.03623036 NaN 0.02799885
## 0.86974900 0.03623036 NaN 0.02799885
## 0.91116276 0.03623036 NaN 0.02799885
## 0.95454846 0.03623036 NaN 0.02799885
## 1.00000000 0.03623036 NaN 0.02799885
##
## Tuning parameter 'alpha' was held constant at a value of 1
## RMSE was used to select the optimal model using the smallest value.
## The final values used for the model were alpha = 1 and lambda = 0.01.
## alpha lambda
## 1 1 0.01
## alpha lambda RMSE Rsquared MAE RMSESD RsquaredSD
## 1 1 0.01000000 0.03553800 0.114963 0.02750353 0.001405490 0.02115810
## 2 1 0.01047616 0.03567477 0.114963 0.02759932 0.001410508 0.02115810
## 3 1 0.01097499 0.03582429 0.114963 0.02770616 0.001415695 0.02115810
## 4 1 0.01149757 0.03598768 0.114963 0.02782456 0.001421054 0.02115810
## 5 1 0.01204504 0.03616167 0.109576 0.02795007 0.001422758 0.01330986
## 6 1 0.01261857 0.03623036 NaN 0.02799885 0.001400273 NA
## 7 1 0.01321941 0.03623036 NaN 0.02799885 0.001400273 NA
## 8 1 0.01384886 0.03623036 NaN 0.02799885 0.001400273 NA
## 9 1 0.01450829 0.03623036 NaN 0.02799885 0.001400273 NA
## 10 1 0.01519911 0.03623036 NaN 0.02799885 0.001400273 NA
## 11 1 0.01592283 0.03623036 NaN 0.02799885 0.001400273 NA
## 12 1 0.01668101 0.03623036 NaN 0.02799885 0.001400273 NA
## 13 1 0.01747528 0.03623036 NaN 0.02799885 0.001400273 NA
## 14 1 0.01830738 0.03623036 NaN 0.02799885 0.001400273 NA
## 15 1 0.01917910 0.03623036 NaN 0.02799885 0.001400273 NA
## 16 1 0.02009233 0.03623036 NaN 0.02799885 0.001400273 NA
## 17 1 0.02104904 0.03623036 NaN 0.02799885 0.001400273 NA
## 18 1 0.02205131 0.03623036 NaN 0.02799885 0.001400273 NA
## 19 1 0.02310130 0.03623036 NaN 0.02799885 0.001400273 NA
## 20 1 0.02420128 0.03623036 NaN 0.02799885 0.001400273 NA
## 21 1 0.02535364 0.03623036 NaN 0.02799885 0.001400273 NA
## 22 1 0.02656088 0.03623036 NaN 0.02799885 0.001400273 NA
## 23 1 0.02782559 0.03623036 NaN 0.02799885 0.001400273 NA
## 24 1 0.02915053 0.03623036 NaN 0.02799885 0.001400273 NA
## 25 1 0.03053856 0.03623036 NaN 0.02799885 0.001400273 NA
## 26 1 0.03199267 0.03623036 NaN 0.02799885 0.001400273 NA
## 27 1 0.03351603 0.03623036 NaN 0.02799885 0.001400273 NA
## 28 1 0.03511192 0.03623036 NaN 0.02799885 0.001400273 NA
## 29 1 0.03678380 0.03623036 NaN 0.02799885 0.001400273 NA
## 30 1 0.03853529 0.03623036 NaN 0.02799885 0.001400273 NA
## 31 1 0.04037017 0.03623036 NaN 0.02799885 0.001400273 NA
## 32 1 0.04229243 0.03623036 NaN 0.02799885 0.001400273 NA
## 33 1 0.04430621 0.03623036 NaN 0.02799885 0.001400273 NA
## 34 1 0.04641589 0.03623036 NaN 0.02799885 0.001400273 NA
## 35 1 0.04862602 0.03623036 NaN 0.02799885 0.001400273 NA
## 36 1 0.05094138 0.03623036 NaN 0.02799885 0.001400273 NA
## 37 1 0.05336699 0.03623036 NaN 0.02799885 0.001400273 NA
## 38 1 0.05590810 0.03623036 NaN 0.02799885 0.001400273 NA
## 39 1 0.05857021 0.03623036 NaN 0.02799885 0.001400273 NA
## 40 1 0.06135907 0.03623036 NaN 0.02799885 0.001400273 NA
## 41 1 0.06428073 0.03623036 NaN 0.02799885 0.001400273 NA
## 42 1 0.06734151 0.03623036 NaN 0.02799885 0.001400273 NA
## 43 1 0.07054802 0.03623036 NaN 0.02799885 0.001400273 NA
## 44 1 0.07390722 0.03623036 NaN 0.02799885 0.001400273 NA
## 45 1 0.07742637 0.03623036 NaN 0.02799885 0.001400273 NA
## 46 1 0.08111308 0.03623036 NaN 0.02799885 0.001400273 NA
## 47 1 0.08497534 0.03623036 NaN 0.02799885 0.001400273 NA
## 48 1 0.08902151 0.03623036 NaN 0.02799885 0.001400273 NA
## 49 1 0.09326033 0.03623036 NaN 0.02799885 0.001400273 NA
## 50 1 0.09770100 0.03623036 NaN 0.02799885 0.001400273 NA
## 51 1 0.10235310 0.03623036 NaN 0.02799885 0.001400273 NA
## 52 1 0.10722672 0.03623036 NaN 0.02799885 0.001400273 NA
## 53 1 0.11233240 0.03623036 NaN 0.02799885 0.001400273 NA
## 54 1 0.11768120 0.03623036 NaN 0.02799885 0.001400273 NA
## 55 1 0.12328467 0.03623036 NaN 0.02799885 0.001400273 NA
## 56 1 0.12915497 0.03623036 NaN 0.02799885 0.001400273 NA
## 57 1 0.13530478 0.03623036 NaN 0.02799885 0.001400273 NA
## 58 1 0.14174742 0.03623036 NaN 0.02799885 0.001400273 NA
## 59 1 0.14849683 0.03623036 NaN 0.02799885 0.001400273 NA
## 60 1 0.15556761 0.03623036 NaN 0.02799885 0.001400273 NA
## 61 1 0.16297508 0.03623036 NaN 0.02799885 0.001400273 NA
## 62 1 0.17073526 0.03623036 NaN 0.02799885 0.001400273 NA
## 63 1 0.17886495 0.03623036 NaN 0.02799885 0.001400273 NA
## 64 1 0.18738174 0.03623036 NaN 0.02799885 0.001400273 NA
## 65 1 0.19630407 0.03623036 NaN 0.02799885 0.001400273 NA
## 66 1 0.20565123 0.03623036 NaN 0.02799885 0.001400273 NA
## 67 1 0.21544347 0.03623036 NaN 0.02799885 0.001400273 NA
## 68 1 0.22570197 0.03623036 NaN 0.02799885 0.001400273 NA
## 69 1 0.23644894 0.03623036 NaN 0.02799885 0.001400273 NA
## 70 1 0.24770764 0.03623036 NaN 0.02799885 0.001400273 NA
## 71 1 0.25950242 0.03623036 NaN 0.02799885 0.001400273 NA
## 72 1 0.27185882 0.03623036 NaN 0.02799885 0.001400273 NA
## 73 1 0.28480359 0.03623036 NaN 0.02799885 0.001400273 NA
## 74 1 0.29836472 0.03623036 NaN 0.02799885 0.001400273 NA
## 75 1 0.31257158 0.03623036 NaN 0.02799885 0.001400273 NA
## 76 1 0.32745492 0.03623036 NaN 0.02799885 0.001400273 NA
## 77 1 0.34304693 0.03623036 NaN 0.02799885 0.001400273 NA
## 78 1 0.35938137 0.03623036 NaN 0.02799885 0.001400273 NA
## 79 1 0.37649358 0.03623036 NaN 0.02799885 0.001400273 NA
## 80 1 0.39442061 0.03623036 NaN 0.02799885 0.001400273 NA
## 81 1 0.41320124 0.03623036 NaN 0.02799885 0.001400273 NA
## 82 1 0.43287613 0.03623036 NaN 0.02799885 0.001400273 NA
## 83 1 0.45348785 0.03623036 NaN 0.02799885 0.001400273 NA
## 84 1 0.47508102 0.03623036 NaN 0.02799885 0.001400273 NA
## 85 1 0.49770236 0.03623036 NaN 0.02799885 0.001400273 NA
## 86 1 0.52140083 0.03623036 NaN 0.02799885 0.001400273 NA
## 87 1 0.54622772 0.03623036 NaN 0.02799885 0.001400273 NA
## 88 1 0.57223677 0.03623036 NaN 0.02799885 0.001400273 NA
## 89 1 0.59948425 0.03623036 NaN 0.02799885 0.001400273 NA
## 90 1 0.62802914 0.03623036 NaN 0.02799885 0.001400273 NA
## 91 1 0.65793322 0.03623036 NaN 0.02799885 0.001400273 NA
## 92 1 0.68926121 0.03623036 NaN 0.02799885 0.001400273 NA
## 93 1 0.72208090 0.03623036 NaN 0.02799885 0.001400273 NA
## 94 1 0.75646333 0.03623036 NaN 0.02799885 0.001400273 NA
## 95 1 0.79248290 0.03623036 NaN 0.02799885 0.001400273 NA
## 96 1 0.83021757 0.03623036 NaN 0.02799885 0.001400273 NA
## 97 1 0.86974900 0.03623036 NaN 0.02799885 0.001400273 NA
## 98 1 0.91116276 0.03623036 NaN 0.02799885 0.001400273 NA
## 99 1 0.95454846 0.03623036 NaN 0.02799885 0.001400273 NA
## 100 1 1.00000000 0.03623036 NaN 0.02799885 0.001400273 NA
## MAESD
## 1 0.0006664622
## 2 0.0006771143
## 3 0.0006882489
## 4 0.0007001831
## 5 0.0007105293
## 6 0.0006972770
## 7 0.0006972770
## 8 0.0006972770
## 9 0.0006972770
## 10 0.0006972770
## 11 0.0006972770
## 12 0.0006972770
## 13 0.0006972770
## 14 0.0006972770
## 15 0.0006972770
## 16 0.0006972770
## 17 0.0006972770
## 18 0.0006972770
## 19 0.0006972770
## 20 0.0006972770
## 21 0.0006972770
## 22 0.0006972770
## 23 0.0006972770
## 24 0.0006972770
## 25 0.0006972770
## 26 0.0006972770
## 27 0.0006972770
## 28 0.0006972770
## 29 0.0006972770
## 30 0.0006972770
## 31 0.0006972770
## 32 0.0006972770
## 33 0.0006972770
## 34 0.0006972770
## 35 0.0006972770
## 36 0.0006972770
## 37 0.0006972770
## 38 0.0006972770
## 39 0.0006972770
## 40 0.0006972770
## 41 0.0006972770
## 42 0.0006972770
## 43 0.0006972770
## 44 0.0006972770
## 45 0.0006972770
## 46 0.0006972770
## 47 0.0006972770
## 48 0.0006972770
## 49 0.0006972770
## 50 0.0006972770
## 51 0.0006972770
## 52 0.0006972770
## 53 0.0006972770
## 54 0.0006972770
## 55 0.0006972770
## 56 0.0006972770
## 57 0.0006972770
## 58 0.0006972770
## 59 0.0006972770
## 60 0.0006972770
## 61 0.0006972770
## 62 0.0006972770
## 63 0.0006972770
## 64 0.0006972770
## 65 0.0006972770
## 66 0.0006972770
## 67 0.0006972770
## 68 0.0006972770
## 69 0.0006972770
## 70 0.0006972770
## 71 0.0006972770
## 72 0.0006972770
## 73 0.0006972770
## 74 0.0006972770
## 75 0.0006972770
## 76 0.0006972770
## 77 0.0006972770
## 78 0.0006972770
## 79 0.0006972770
## 80 0.0006972770
## 81 0.0006972770
## 82 0.0006972770
## 83 0.0006972770
## 84 0.0006972770
## 85 0.0006972770
## 86 0.0006972770
## 87 0.0006972770
## 88 0.0006972770
## 89 0.0006972770
## 90 0.0006972770
## 91 0.0006972770
## 92 0.0006972770
## 93 0.0006972770
## 94 0.0006972770
## 95 0.0006972770
## 96 0.0006972770
## 97 0.0006972770
## 98 0.0006972770
## 99 0.0006972770
## 100 0.0006972770## Warning: Removed 95 rows containing missing values (geom_path).## Warning: Removed 95 rows containing missing values (geom_point).
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Test
if (algo.LASSO.caret == TRUE){
test.model(model.LASSO.caret, data.test
,method = 'glmnet',subopt = "LASSO"
,formula = formula, feature.names = feature.names, label.names = label.names
,draw.limits = TRUE, transformation = t)
}## [1] "Summary of predicted values: "
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.092 2.094 2.096 2.096 2.098 2.100
## [1] "glmnet LASSO Test MSE: 0.00127010962901025"
LASSO with CV (w/ filtered train)
Train
if (algo.LASSO.caret == TRUE){
set.seed(1)
returned = train.caret.glmselect(formula = formula
,data = data.train2
,method = "glmnet"
,subopt = 'LASSO'
,feature.names = feature.names)
model.LASSO.caret = returned$model
}## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.01 on full training set
## glmnet
##
## 5708 samples
## 240 predictor
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 5137, 5137, 5137, 5137, 5136, 5137, ...
## Resampling results across tuning parameters:
##
## lambda RMSE Rsquared MAE
## 0.01000000 0.03056302 0.1564097 0.02441058
## 0.01047616 0.03072207 0.1564097 0.02451832
## 0.01097499 0.03089567 0.1564097 0.02463707
## 0.01149757 0.03108508 0.1564097 0.02476805
## 0.01204504 0.03129164 0.1564097 0.02491344
## 0.01261857 0.03141724 NaN 0.02500165
## 0.01321941 0.03141724 NaN 0.02500165
## 0.01384886 0.03141724 NaN 0.02500165
## 0.01450829 0.03141724 NaN 0.02500165
## 0.01519911 0.03141724 NaN 0.02500165
## 0.01592283 0.03141724 NaN 0.02500165
## 0.01668101 0.03141724 NaN 0.02500165
## 0.01747528 0.03141724 NaN 0.02500165
## 0.01830738 0.03141724 NaN 0.02500165
## 0.01917910 0.03141724 NaN 0.02500165
## 0.02009233 0.03141724 NaN 0.02500165
## 0.02104904 0.03141724 NaN 0.02500165
## 0.02205131 0.03141724 NaN 0.02500165
## 0.02310130 0.03141724 NaN 0.02500165
## 0.02420128 0.03141724 NaN 0.02500165
## 0.02535364 0.03141724 NaN 0.02500165
## 0.02656088 0.03141724 NaN 0.02500165
## 0.02782559 0.03141724 NaN 0.02500165
## 0.02915053 0.03141724 NaN 0.02500165
## 0.03053856 0.03141724 NaN 0.02500165
## 0.03199267 0.03141724 NaN 0.02500165
## 0.03351603 0.03141724 NaN 0.02500165
## 0.03511192 0.03141724 NaN 0.02500165
## 0.03678380 0.03141724 NaN 0.02500165
## 0.03853529 0.03141724 NaN 0.02500165
## 0.04037017 0.03141724 NaN 0.02500165
## 0.04229243 0.03141724 NaN 0.02500165
## 0.04430621 0.03141724 NaN 0.02500165
## 0.04641589 0.03141724 NaN 0.02500165
## 0.04862602 0.03141724 NaN 0.02500165
## 0.05094138 0.03141724 NaN 0.02500165
## 0.05336699 0.03141724 NaN 0.02500165
## 0.05590810 0.03141724 NaN 0.02500165
## 0.05857021 0.03141724 NaN 0.02500165
## 0.06135907 0.03141724 NaN 0.02500165
## 0.06428073 0.03141724 NaN 0.02500165
## 0.06734151 0.03141724 NaN 0.02500165
## 0.07054802 0.03141724 NaN 0.02500165
## 0.07390722 0.03141724 NaN 0.02500165
## 0.07742637 0.03141724 NaN 0.02500165
## 0.08111308 0.03141724 NaN 0.02500165
## 0.08497534 0.03141724 NaN 0.02500165
## 0.08902151 0.03141724 NaN 0.02500165
## 0.09326033 0.03141724 NaN 0.02500165
## 0.09770100 0.03141724 NaN 0.02500165
## 0.10235310 0.03141724 NaN 0.02500165
## 0.10722672 0.03141724 NaN 0.02500165
## 0.11233240 0.03141724 NaN 0.02500165
## 0.11768120 0.03141724 NaN 0.02500165
## 0.12328467 0.03141724 NaN 0.02500165
## 0.12915497 0.03141724 NaN 0.02500165
## 0.13530478 0.03141724 NaN 0.02500165
## 0.14174742 0.03141724 NaN 0.02500165
## 0.14849683 0.03141724 NaN 0.02500165
## 0.15556761 0.03141724 NaN 0.02500165
## 0.16297508 0.03141724 NaN 0.02500165
## 0.17073526 0.03141724 NaN 0.02500165
## 0.17886495 0.03141724 NaN 0.02500165
## 0.18738174 0.03141724 NaN 0.02500165
## 0.19630407 0.03141724 NaN 0.02500165
## 0.20565123 0.03141724 NaN 0.02500165
## 0.21544347 0.03141724 NaN 0.02500165
## 0.22570197 0.03141724 NaN 0.02500165
## 0.23644894 0.03141724 NaN 0.02500165
## 0.24770764 0.03141724 NaN 0.02500165
## 0.25950242 0.03141724 NaN 0.02500165
## 0.27185882 0.03141724 NaN 0.02500165
## 0.28480359 0.03141724 NaN 0.02500165
## 0.29836472 0.03141724 NaN 0.02500165
## 0.31257158 0.03141724 NaN 0.02500165
## 0.32745492 0.03141724 NaN 0.02500165
## 0.34304693 0.03141724 NaN 0.02500165
## 0.35938137 0.03141724 NaN 0.02500165
## 0.37649358 0.03141724 NaN 0.02500165
## 0.39442061 0.03141724 NaN 0.02500165
## 0.41320124 0.03141724 NaN 0.02500165
## 0.43287613 0.03141724 NaN 0.02500165
## 0.45348785 0.03141724 NaN 0.02500165
## 0.47508102 0.03141724 NaN 0.02500165
## 0.49770236 0.03141724 NaN 0.02500165
## 0.52140083 0.03141724 NaN 0.02500165
## 0.54622772 0.03141724 NaN 0.02500165
## 0.57223677 0.03141724 NaN 0.02500165
## 0.59948425 0.03141724 NaN 0.02500165
## 0.62802914 0.03141724 NaN 0.02500165
## 0.65793322 0.03141724 NaN 0.02500165
## 0.68926121 0.03141724 NaN 0.02500165
## 0.72208090 0.03141724 NaN 0.02500165
## 0.75646333 0.03141724 NaN 0.02500165
## 0.79248290 0.03141724 NaN 0.02500165
## 0.83021757 0.03141724 NaN 0.02500165
## 0.86974900 0.03141724 NaN 0.02500165
## 0.91116276 0.03141724 NaN 0.02500165
## 0.95454846 0.03141724 NaN 0.02500165
## 1.00000000 0.03141724 NaN 0.02500165
##
## Tuning parameter 'alpha' was held constant at a value of 1
## RMSE was used to select the optimal model using the smallest value.
## The final values used for the model were alpha = 1 and lambda = 0.01.
## alpha lambda
## 1 1 0.01
## alpha lambda RMSE Rsquared MAE RMSESD
## 1 1 0.01000000 0.03056302 0.1564097 0.02441058 0.0005568878
## 2 1 0.01047616 0.03072207 0.1564097 0.02451832 0.0005602007
## 3 1 0.01097499 0.03089567 0.1564097 0.02463707 0.0005643146
## 4 1 0.01149757 0.03108508 0.1564097 0.02476805 0.0005693060
## 5 1 0.01204504 0.03129164 0.1564097 0.02491344 0.0005752517
## 6 1 0.01261857 0.03141724 NaN 0.02500165 0.0005633091
## 7 1 0.01321941 0.03141724 NaN 0.02500165 0.0005633091
## 8 1 0.01384886 0.03141724 NaN 0.02500165 0.0005633091
## 9 1 0.01450829 0.03141724 NaN 0.02500165 0.0005633091
## 10 1 0.01519911 0.03141724 NaN 0.02500165 0.0005633091
## 11 1 0.01592283 0.03141724 NaN 0.02500165 0.0005633091
## 12 1 0.01668101 0.03141724 NaN 0.02500165 0.0005633091
## 13 1 0.01747528 0.03141724 NaN 0.02500165 0.0005633091
## 14 1 0.01830738 0.03141724 NaN 0.02500165 0.0005633091
## 15 1 0.01917910 0.03141724 NaN 0.02500165 0.0005633091
## 16 1 0.02009233 0.03141724 NaN 0.02500165 0.0005633091
## 17 1 0.02104904 0.03141724 NaN 0.02500165 0.0005633091
## 18 1 0.02205131 0.03141724 NaN 0.02500165 0.0005633091
## 19 1 0.02310130 0.03141724 NaN 0.02500165 0.0005633091
## 20 1 0.02420128 0.03141724 NaN 0.02500165 0.0005633091
## 21 1 0.02535364 0.03141724 NaN 0.02500165 0.0005633091
## 22 1 0.02656088 0.03141724 NaN 0.02500165 0.0005633091
## 23 1 0.02782559 0.03141724 NaN 0.02500165 0.0005633091
## 24 1 0.02915053 0.03141724 NaN 0.02500165 0.0005633091
## 25 1 0.03053856 0.03141724 NaN 0.02500165 0.0005633091
## 26 1 0.03199267 0.03141724 NaN 0.02500165 0.0005633091
## 27 1 0.03351603 0.03141724 NaN 0.02500165 0.0005633091
## 28 1 0.03511192 0.03141724 NaN 0.02500165 0.0005633091
## 29 1 0.03678380 0.03141724 NaN 0.02500165 0.0005633091
## 30 1 0.03853529 0.03141724 NaN 0.02500165 0.0005633091
## 31 1 0.04037017 0.03141724 NaN 0.02500165 0.0005633091
## 32 1 0.04229243 0.03141724 NaN 0.02500165 0.0005633091
## 33 1 0.04430621 0.03141724 NaN 0.02500165 0.0005633091
## 34 1 0.04641589 0.03141724 NaN 0.02500165 0.0005633091
## 35 1 0.04862602 0.03141724 NaN 0.02500165 0.0005633091
## 36 1 0.05094138 0.03141724 NaN 0.02500165 0.0005633091
## 37 1 0.05336699 0.03141724 NaN 0.02500165 0.0005633091
## 38 1 0.05590810 0.03141724 NaN 0.02500165 0.0005633091
## 39 1 0.05857021 0.03141724 NaN 0.02500165 0.0005633091
## 40 1 0.06135907 0.03141724 NaN 0.02500165 0.0005633091
## 41 1 0.06428073 0.03141724 NaN 0.02500165 0.0005633091
## 42 1 0.06734151 0.03141724 NaN 0.02500165 0.0005633091
## 43 1 0.07054802 0.03141724 NaN 0.02500165 0.0005633091
## 44 1 0.07390722 0.03141724 NaN 0.02500165 0.0005633091
## 45 1 0.07742637 0.03141724 NaN 0.02500165 0.0005633091
## 46 1 0.08111308 0.03141724 NaN 0.02500165 0.0005633091
## 47 1 0.08497534 0.03141724 NaN 0.02500165 0.0005633091
## 48 1 0.08902151 0.03141724 NaN 0.02500165 0.0005633091
## 49 1 0.09326033 0.03141724 NaN 0.02500165 0.0005633091
## 50 1 0.09770100 0.03141724 NaN 0.02500165 0.0005633091
## 51 1 0.10235310 0.03141724 NaN 0.02500165 0.0005633091
## 52 1 0.10722672 0.03141724 NaN 0.02500165 0.0005633091
## 53 1 0.11233240 0.03141724 NaN 0.02500165 0.0005633091
## 54 1 0.11768120 0.03141724 NaN 0.02500165 0.0005633091
## 55 1 0.12328467 0.03141724 NaN 0.02500165 0.0005633091
## 56 1 0.12915497 0.03141724 NaN 0.02500165 0.0005633091
## 57 1 0.13530478 0.03141724 NaN 0.02500165 0.0005633091
## 58 1 0.14174742 0.03141724 NaN 0.02500165 0.0005633091
## 59 1 0.14849683 0.03141724 NaN 0.02500165 0.0005633091
## 60 1 0.15556761 0.03141724 NaN 0.02500165 0.0005633091
## 61 1 0.16297508 0.03141724 NaN 0.02500165 0.0005633091
## 62 1 0.17073526 0.03141724 NaN 0.02500165 0.0005633091
## 63 1 0.17886495 0.03141724 NaN 0.02500165 0.0005633091
## 64 1 0.18738174 0.03141724 NaN 0.02500165 0.0005633091
## 65 1 0.19630407 0.03141724 NaN 0.02500165 0.0005633091
## 66 1 0.20565123 0.03141724 NaN 0.02500165 0.0005633091
## 67 1 0.21544347 0.03141724 NaN 0.02500165 0.0005633091
## 68 1 0.22570197 0.03141724 NaN 0.02500165 0.0005633091
## 69 1 0.23644894 0.03141724 NaN 0.02500165 0.0005633091
## 70 1 0.24770764 0.03141724 NaN 0.02500165 0.0005633091
## 71 1 0.25950242 0.03141724 NaN 0.02500165 0.0005633091
## 72 1 0.27185882 0.03141724 NaN 0.02500165 0.0005633091
## 73 1 0.28480359 0.03141724 NaN 0.02500165 0.0005633091
## 74 1 0.29836472 0.03141724 NaN 0.02500165 0.0005633091
## 75 1 0.31257158 0.03141724 NaN 0.02500165 0.0005633091
## 76 1 0.32745492 0.03141724 NaN 0.02500165 0.0005633091
## 77 1 0.34304693 0.03141724 NaN 0.02500165 0.0005633091
## 78 1 0.35938137 0.03141724 NaN 0.02500165 0.0005633091
## 79 1 0.37649358 0.03141724 NaN 0.02500165 0.0005633091
## 80 1 0.39442061 0.03141724 NaN 0.02500165 0.0005633091
## 81 1 0.41320124 0.03141724 NaN 0.02500165 0.0005633091
## 82 1 0.43287613 0.03141724 NaN 0.02500165 0.0005633091
## 83 1 0.45348785 0.03141724 NaN 0.02500165 0.0005633091
## 84 1 0.47508102 0.03141724 NaN 0.02500165 0.0005633091
## 85 1 0.49770236 0.03141724 NaN 0.02500165 0.0005633091
## 86 1 0.52140083 0.03141724 NaN 0.02500165 0.0005633091
## 87 1 0.54622772 0.03141724 NaN 0.02500165 0.0005633091
## 88 1 0.57223677 0.03141724 NaN 0.02500165 0.0005633091
## 89 1 0.59948425 0.03141724 NaN 0.02500165 0.0005633091
## 90 1 0.62802914 0.03141724 NaN 0.02500165 0.0005633091
## 91 1 0.65793322 0.03141724 NaN 0.02500165 0.0005633091
## 92 1 0.68926121 0.03141724 NaN 0.02500165 0.0005633091
## 93 1 0.72208090 0.03141724 NaN 0.02500165 0.0005633091
## 94 1 0.75646333 0.03141724 NaN 0.02500165 0.0005633091
## 95 1 0.79248290 0.03141724 NaN 0.02500165 0.0005633091
## 96 1 0.83021757 0.03141724 NaN 0.02500165 0.0005633091
## 97 1 0.86974900 0.03141724 NaN 0.02500165 0.0005633091
## 98 1 0.91116276 0.03141724 NaN 0.02500165 0.0005633091
## 99 1 0.95454846 0.03141724 NaN 0.02500165 0.0005633091
## 100 1 1.00000000 0.03141724 NaN 0.02500165 0.0005633091
## RsquaredSD MAESD
## 1 0.02633141 0.0003830351
## 2 0.02633141 0.0003827229
## 3 0.02633141 0.0003822826
## 4 0.02633141 0.0003839783
## 5 0.02633141 0.0003856794
## 6 NA 0.0003743830
## 7 NA 0.0003743830
## 8 NA 0.0003743830
## 9 NA 0.0003743830
## 10 NA 0.0003743830
## 11 NA 0.0003743830
## 12 NA 0.0003743830
## 13 NA 0.0003743830
## 14 NA 0.0003743830
## 15 NA 0.0003743830
## 16 NA 0.0003743830
## 17 NA 0.0003743830
## 18 NA 0.0003743830
## 19 NA 0.0003743830
## 20 NA 0.0003743830
## 21 NA 0.0003743830
## 22 NA 0.0003743830
## 23 NA 0.0003743830
## 24 NA 0.0003743830
## 25 NA 0.0003743830
## 26 NA 0.0003743830
## 27 NA 0.0003743830
## 28 NA 0.0003743830
## 29 NA 0.0003743830
## 30 NA 0.0003743830
## 31 NA 0.0003743830
## 32 NA 0.0003743830
## 33 NA 0.0003743830
## 34 NA 0.0003743830
## 35 NA 0.0003743830
## 36 NA 0.0003743830
## 37 NA 0.0003743830
## 38 NA 0.0003743830
## 39 NA 0.0003743830
## 40 NA 0.0003743830
## 41 NA 0.0003743830
## 42 NA 0.0003743830
## 43 NA 0.0003743830
## 44 NA 0.0003743830
## 45 NA 0.0003743830
## 46 NA 0.0003743830
## 47 NA 0.0003743830
## 48 NA 0.0003743830
## 49 NA 0.0003743830
## 50 NA 0.0003743830
## 51 NA 0.0003743830
## 52 NA 0.0003743830
## 53 NA 0.0003743830
## 54 NA 0.0003743830
## 55 NA 0.0003743830
## 56 NA 0.0003743830
## 57 NA 0.0003743830
## 58 NA 0.0003743830
## 59 NA 0.0003743830
## 60 NA 0.0003743830
## 61 NA 0.0003743830
## 62 NA 0.0003743830
## 63 NA 0.0003743830
## 64 NA 0.0003743830
## 65 NA 0.0003743830
## 66 NA 0.0003743830
## 67 NA 0.0003743830
## 68 NA 0.0003743830
## 69 NA 0.0003743830
## 70 NA 0.0003743830
## 71 NA 0.0003743830
## 72 NA 0.0003743830
## 73 NA 0.0003743830
## 74 NA 0.0003743830
## 75 NA 0.0003743830
## 76 NA 0.0003743830
## 77 NA 0.0003743830
## 78 NA 0.0003743830
## 79 NA 0.0003743830
## 80 NA 0.0003743830
## 81 NA 0.0003743830
## 82 NA 0.0003743830
## 83 NA 0.0003743830
## 84 NA 0.0003743830
## 85 NA 0.0003743830
## 86 NA 0.0003743830
## 87 NA 0.0003743830
## 88 NA 0.0003743830
## 89 NA 0.0003743830
## 90 NA 0.0003743830
## 91 NA 0.0003743830
## 92 NA 0.0003743830
## 93 NA 0.0003743830
## 94 NA 0.0003743830
## 95 NA 0.0003743830
## 96 NA 0.0003743830
## 97 NA 0.0003743830
## 98 NA 0.0003743830
## 99 NA 0.0003743830
## 100 NA 0.0003743830## Warning: Removed 95 rows containing missing values (geom_path).## Warning: Removed 95 rows containing missing values (geom_point).
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Test
if (algo.LASSO.caret == TRUE){
test.model(model.LASSO.caret, data.test
,method = 'glmnet',subopt = "LASSO"
,formula = formula, feature.names = feature.names, label.names = label.names
,draw.limits = TRUE, transformation = t)
}## [1] "Summary of predicted values: "
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.088 2.091 2.093 2.093 2.095 2.096
## [1] "glmnet LASSO Test MSE: 0.00129681819581809"
LARS with CV (w/ full train)
Train
if (algo.LARS.caret == TRUE){
set.seed(1)
returned = train.caret.glmselect(formula = formula
,data = data.train
,method = "lars"
,subopt = 'NULL'
,feature.names = feature.names)
model.LARS.caret = returned$model
}## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.## Aggregating results
## Selecting tuning parameters
## Fitting fraction = 0.434 on full training set
## Least Angle Regression
##
## 6002 samples
## 240 predictor
##
## Pre-processing: centered (240), scaled (240)
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 5402, 5401, 5402, 5401, 5402, 5402, ...
## Resampling results across tuning parameters:
##
## fraction RMSE Rsquared MAE
## 0.00000000 0.03623036 NaN 0.02799885
## 0.01010101 0.03579743 0.1149630 0.02768458
## 0.02020202 0.03541053 0.1149630 0.02741268
## 0.03030303 0.03507118 0.1149630 0.02718320
## 0.04040404 0.03478390 0.1236994 0.02698727
## 0.05050505 0.03451932 0.1367743 0.02679842
## 0.06060606 0.03427727 0.1500572 0.02661859
## 0.07070707 0.03405292 0.1619525 0.02644719
## 0.08080808 0.03383771 0.1730473 0.02627791
## 0.09090909 0.03363338 0.1819930 0.02611208
## 0.10101010 0.03344302 0.1890156 0.02595560
## 0.11111111 0.03326631 0.1955805 0.02581076
## 0.12121212 0.03309716 0.2020083 0.02567045
## 0.13131313 0.03293831 0.2072679 0.02553607
## 0.14141414 0.03278988 0.2115510 0.02540726
## 0.15151515 0.03265203 0.2150239 0.02528355
## 0.16161616 0.03252489 0.2178273 0.02516535
## 0.17171717 0.03240859 0.2200792 0.02505349
## 0.18181818 0.03230324 0.2218774 0.02494803
## 0.19191919 0.03221276 0.2233206 0.02485479
## 0.20202020 0.03213541 0.2247774 0.02477309
## 0.21212121 0.03206534 0.2263885 0.02469948
## 0.22222222 0.03200681 0.2278136 0.02463694
## 0.23232323 0.03195556 0.2290974 0.02458427
## 0.24242424 0.03191136 0.2303060 0.02454161
## 0.25252525 0.03187379 0.2313632 0.02450416
## 0.26262626 0.03184318 0.2322271 0.02447245
## 0.27272727 0.03181646 0.2330026 0.02444481
## 0.28282828 0.03179321 0.2337110 0.02442083
## 0.29292929 0.03177256 0.2343564 0.02440039
## 0.30303030 0.03175505 0.2349000 0.02438262
## 0.31313131 0.03174090 0.2353169 0.02436704
## 0.32323232 0.03173000 0.2356047 0.02435416
## 0.33333333 0.03172172 0.2357822 0.02434397
## 0.34343434 0.03171588 0.2358547 0.02433594
## 0.35353535 0.03171146 0.2358763 0.02432924
## 0.36363636 0.03170816 0.2358617 0.02432340
## 0.37373737 0.03170562 0.2358296 0.02431845
## 0.38383838 0.03170297 0.2358274 0.02431402
## 0.39393939 0.03170046 0.2358354 0.02430997
## 0.40404040 0.03169876 0.2358156 0.02430620
## 0.41414141 0.03169704 0.2358114 0.02430256
## 0.42424242 0.03169583 0.2357894 0.02429971
## 0.43434343 0.03169527 0.2357443 0.02429779
## 0.44444444 0.03169568 0.2356592 0.02429647
## 0.45454545 0.03169740 0.2355153 0.02429626
## 0.46464646 0.03170015 0.2353268 0.02429664
## 0.47474747 0.03170361 0.2351097 0.02429804
## 0.48484848 0.03170764 0.2348697 0.02430004
## 0.49494949 0.03171185 0.2346274 0.02430255
## 0.50505051 0.03171664 0.2343603 0.02430561
## 0.51515152 0.03172177 0.2340838 0.02430875
## 0.52525253 0.03172673 0.2338228 0.02431190
## 0.53535354 0.03173220 0.2335435 0.02431543
## 0.54545455 0.03173785 0.2332618 0.02431910
## 0.55555556 0.03174404 0.2329588 0.02432339
## 0.56565657 0.03175035 0.2326565 0.02432757
## 0.57575758 0.03175676 0.2323548 0.02433184
## 0.58585859 0.03176368 0.2320336 0.02433640
## 0.59595960 0.03177098 0.2316985 0.02434107
## 0.60606061 0.03177885 0.2313418 0.02434603
## 0.61616162 0.03178695 0.2309790 0.02435112
## 0.62626263 0.03179541 0.2306042 0.02435629
## 0.63636364 0.03180435 0.2302119 0.02436191
## 0.64646465 0.03181337 0.2298207 0.02436795
## 0.65656566 0.03182257 0.2294261 0.02437436
## 0.66666667 0.03183176 0.2290361 0.02438065
## 0.67676768 0.03184096 0.2286505 0.02438686
## 0.68686869 0.03185028 0.2282648 0.02439335
## 0.69696970 0.03185987 0.2278721 0.02440016
## 0.70707071 0.03186958 0.2274789 0.02440694
## 0.71717172 0.03187956 0.2270778 0.02441389
## 0.72727273 0.03188996 0.2266624 0.02442126
## 0.73737374 0.03190077 0.2262321 0.02442902
## 0.74747475 0.03191201 0.2257872 0.02443689
## 0.75757576 0.03192330 0.2253447 0.02444471
## 0.76767677 0.03193465 0.2249038 0.02445271
## 0.77777778 0.03194609 0.2244622 0.02446107
## 0.78787879 0.03195762 0.2240210 0.02446972
## 0.79797980 0.03196908 0.2235864 0.02447835
## 0.80808081 0.03198079 0.2231445 0.02448700
## 0.81818182 0.03199264 0.2227009 0.02449577
## 0.82828283 0.03200476 0.2222505 0.02450493
## 0.83838384 0.03201697 0.2218007 0.02451408
## 0.84848485 0.03202936 0.2213467 0.02452347
## 0.85858586 0.03204189 0.2208906 0.02453321
## 0.86868687 0.03205457 0.2204319 0.02454308
## 0.87878788 0.03206742 0.2199696 0.02455319
## 0.88888889 0.03208058 0.2194985 0.02456366
## 0.89898990 0.03209401 0.2190194 0.02457462
## 0.90909091 0.03210769 0.2185344 0.02458598
## 0.91919192 0.03212147 0.2180491 0.02459737
## 0.92929293 0.03213524 0.2175685 0.02460890
## 0.93939394 0.03214921 0.2170836 0.02462059
## 0.94949495 0.03216307 0.2166081 0.02463222
## 0.95959596 0.03217703 0.2161318 0.02464407
## 0.96969697 0.03219113 0.2156541 0.02465605
## 0.97979798 0.03220534 0.2151757 0.02466823
## 0.98989899 0.03221964 0.2146986 0.02468060
## 1.00000000 0.03223413 0.2142186 0.02469321
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was fraction = 0.4343434.
## fraction
## 44 0.4343434## Warning: Removed 1 rows containing missing values (geom_point).
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Test
if (algo.LARS.caret == TRUE){
test.model(model.LARS.caret, data.test
,method = 'lars',subopt = NULL
,formula = formula, feature.names = feature.names, label.names = label.names
,draw.limits = TRUE, transformation = t)
}## [1] "Summary of predicted values: "
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.040 2.085 2.097 2.096 2.108 2.140
## [1] "lars Test MSE: 0.001037729747415"
LARS with CV (w/ filtered train)
Train
if (algo.LARS.caret == TRUE){
set.seed(1)
returned = train.caret.glmselect(formula = formula
,data = data.train2
,method = "lars"
,subopt = 'NULL'
,feature.names = feature.names)
model.LARS.caret = returned$model
}## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.## Aggregating results
## Selecting tuning parameters
## Fitting fraction = 0.576 on full training set
## Least Angle Regression
##
## 5708 samples
## 240 predictor
##
## Pre-processing: centered (240), scaled (240)
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 5137, 5137, 5137, 5137, 5136, 5137, ...
## Resampling results across tuning parameters:
##
## fraction RMSE Rsquared MAE
## 0.00000000 0.03141724 NaN 0.02500165
## 0.01010101 0.03090891 0.1564097 0.02464644
## 0.02020202 0.03045223 0.1564097 0.02433659
## 0.03030303 0.03005282 0.1587862 0.02406843
## 0.04040404 0.02969326 0.1835517 0.02382605
## 0.05050505 0.02935810 0.1996310 0.02360553
## 0.06060606 0.02905377 0.2099558 0.02340730
## 0.07070707 0.02877609 0.2243843 0.02321486
## 0.08080808 0.02850942 0.2400910 0.02301936
## 0.09090909 0.02825560 0.2528172 0.02283019
## 0.10101010 0.02801641 0.2628044 0.02264939
## 0.11111111 0.02779222 0.2706002 0.02247677
## 0.12121212 0.02758682 0.2774220 0.02231724
## 0.13131313 0.02739187 0.2845787 0.02216981
## 0.14141414 0.02720841 0.2906459 0.02203074
## 0.15151515 0.02703759 0.2955867 0.02190019
## 0.16161616 0.02687965 0.2995901 0.02177678
## 0.17171717 0.02673482 0.3028159 0.02165958
## 0.18181818 0.02660424 0.3054709 0.02155058
## 0.19191919 0.02648995 0.3080524 0.02145478
## 0.20202020 0.02638646 0.3109921 0.02137120
## 0.21212121 0.02629158 0.3139147 0.02129357
## 0.22222222 0.02620665 0.3165308 0.02122365
## 0.23232323 0.02613041 0.3190042 0.02116028
## 0.24242424 0.02605849 0.3215870 0.02110524
## 0.25252525 0.02599529 0.3237749 0.02105531
## 0.26262626 0.02594032 0.3256689 0.02101201
## 0.27272727 0.02589192 0.3273732 0.02097417
## 0.28282828 0.02585070 0.3288548 0.02094309
## 0.29292929 0.02581569 0.3300814 0.02091573
## 0.30303030 0.02578552 0.3311309 0.02089133
## 0.31313131 0.02576011 0.3320245 0.02087101
## 0.32323232 0.02573623 0.3328991 0.02085165
## 0.33333333 0.02571368 0.3337491 0.02083257
## 0.34343434 0.02569362 0.3345189 0.02081505
## 0.35353535 0.02567563 0.3352140 0.02079965
## 0.36363636 0.02565917 0.3358478 0.02078532
## 0.37373737 0.02564462 0.3363972 0.02077268
## 0.38383838 0.02563228 0.3368462 0.02076240
## 0.39393939 0.02562149 0.3372304 0.02075367
## 0.40404040 0.02561185 0.3375640 0.02074621
## 0.41414141 0.02560318 0.3378600 0.02073943
## 0.42424242 0.02559480 0.3381522 0.02073299
## 0.43434343 0.02558702 0.3384243 0.02072696
## 0.44444444 0.02558063 0.3386339 0.02072179
## 0.45454545 0.02557469 0.3388311 0.02071715
## 0.46464646 0.02556970 0.3389864 0.02071347
## 0.47474747 0.02556515 0.3391270 0.02071059
## 0.48484848 0.02556176 0.3392129 0.02070879
## 0.49494949 0.02555881 0.3392811 0.02070711
## 0.50505051 0.02555635 0.3393297 0.02070540
## 0.51515152 0.02555427 0.3393633 0.02070407
## 0.52525253 0.02555263 0.3393800 0.02070305
## 0.53535354 0.02555124 0.3393895 0.02070216
## 0.54545455 0.02555033 0.3393789 0.02070161
## 0.55555556 0.02554975 0.3393552 0.02070121
## 0.56565657 0.02554952 0.3393180 0.02070107
## 0.57575758 0.02554936 0.3392814 0.02070072
## 0.58585859 0.02554947 0.3392359 0.02070032
## 0.59595960 0.02555015 0.3391648 0.02070034
## 0.60606061 0.02555156 0.3390597 0.02070091
## 0.61616162 0.02555339 0.3389376 0.02070168
## 0.62626263 0.02555544 0.3388095 0.02070235
## 0.63636364 0.02555764 0.3386775 0.02070312
## 0.64646465 0.02555966 0.3385604 0.02070392
## 0.65656566 0.02556149 0.3384573 0.02070454
## 0.66666667 0.02556376 0.3383354 0.02070563
## 0.67676768 0.02556669 0.3381832 0.02070741
## 0.68686869 0.02556991 0.3380205 0.02070944
## 0.69696970 0.02557313 0.3378616 0.02071132
## 0.70707071 0.02557623 0.3377128 0.02071319
## 0.71717172 0.02557965 0.3375514 0.02071543
## 0.72727273 0.02558352 0.3373715 0.02071801
## 0.73737374 0.02558785 0.3371726 0.02072102
## 0.74747475 0.02559245 0.3369636 0.02072420
## 0.75757576 0.02559745 0.3367387 0.02072781
## 0.76767677 0.02560274 0.3365032 0.02073165
## 0.77777778 0.02560825 0.3362610 0.02073559
## 0.78787879 0.02561389 0.3360173 0.02073949
## 0.79797980 0.02561982 0.3357634 0.02074389
## 0.80808081 0.02562608 0.3354967 0.02074863
## 0.81818182 0.02563260 0.3352210 0.02075366
## 0.82828283 0.02563942 0.3349341 0.02075877
## 0.83838384 0.02564671 0.3346279 0.02076413
## 0.84848485 0.02565420 0.3343162 0.02076960
## 0.85858586 0.02566213 0.3339871 0.02077547
## 0.86868687 0.02567050 0.3336414 0.02078187
## 0.87878788 0.02567924 0.3332814 0.02078844
## 0.88888889 0.02568823 0.3329142 0.02079511
## 0.89898990 0.02569765 0.3325299 0.02080207
## 0.90909091 0.02570747 0.3321304 0.02080927
## 0.91919192 0.02571755 0.3317228 0.02081662
## 0.92929293 0.02572772 0.3313161 0.02082415
## 0.93939394 0.02573805 0.3309058 0.02083194
## 0.94949495 0.02574861 0.3304895 0.02084005
## 0.95959596 0.02575925 0.3300741 0.02084834
## 0.96969697 0.02577004 0.3296557 0.02085677
## 0.97979798 0.02578093 0.3292381 0.02086526
## 0.98989899 0.02579204 0.3288153 0.02087404
## 1.00000000 0.02580348 0.3283813 0.02088310
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was fraction = 0.5757576.
## fraction
## 58 0.5757576## Warning: Removed 1 rows containing missing values (geom_point).
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Test
if (algo.LARS.caret == TRUE){
test.model(model.LARS.caret, data.test
,method = 'lars',subopt = NULL
,formula = formula, feature.names = feature.names, label.names = label.names
,draw.limits = TRUE, transformation = t)
}## [1] "Summary of predicted values: "
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.031 2.081 2.093 2.093 2.105 2.140
## [1] "lars Test MSE: 0.00106433541121342"
